{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\logs\log_publicAB.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}14 Feb 2024, 10:53:35
{txt}
{com}. set maxvar 10000

{txt}
{com}. set scheme stcolor
{txt}
{com}. 
. *This do file requires outreg2, ebalance, fre, coefplot, and ipfweight. 
. *If not installed, uncomment and run the following:
. 
. /*
> ssc install outreg2
> ssc install ebalance
> ssc install coefplot
> ssc install ipfweight
> ssc install fre
> */
. 
. 
. *creating rescale program
. program rescale
{txt}  1{com}.   args oldvar newmin newmax
{txt}  2{com}.   qui sum `oldvar'
{txt}  3{com}.   local oldmin=r(min)
{txt}  4{com}.   local rangequota=(`newmax'-`newmin')/(r(max)-r(min))
{txt}  5{com}.   replace `oldvar'=(`oldvar'-`oldmin')*`rangequota'+`oldmin'+(`newmin'-`oldmin')
{txt}  6{com}. end
{txt}
{com}. 
. *loading AmericasBarometer dataset and adding edr (levels of education), quintall (levels of wealth), and psa5 (system support)
. cd "../data"
{res}C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\data
{txt}
{com}. use "2004-2018 LAPOP AmericasBarometer Merge (v1.0w).dta", clear
{txt}(All data are copyrighted by LAPOP. For more info, run the command note list)

{com}. do psa5_edr_quintall.do // creating edr from ed, quintall from household posessions, and psa5 from b1, b2, b3, b4, b6.
{txt}
{com}. ** creating psa5 (system support)
. egen missing = rowmiss(b1 b2 b3 b4 b6)
{txt}
{com}. egen b1to6 = rowmean(b1 b2 b3 b4 b6) 
{txt}(2,431 missing values generated)

{com}.         replace b1to6=. if missing>2
{txt}(4,775 real changes made, 4,775 to missing)

{com}. recode b1to6 (1/1.79=0)(1.8/3.29=25)(3.3/4.79=50)(4.8/6.29=75)(6.3/7=100), gen(psa5)
{txt}(303,048 differences between {bf:b1to6} and {bf:psa5})

{com}. 
. 
. ** creating edr (levels of education) 
. gen edr=.
{txt}(310,254 missing values generated)

{com}. egen pais1 = cut(ed) if wave<=2018 & pais==1, at(0,1,7,13,30) icode
{txt}(298,064 missing values generated)

{com}. egen pais2 = cut(ed) if wave<=2018 & pais==2, at(0,1,7,13,30) icode
{txt}(298,061 missing values generated)

{com}. egen pais3 = cut(ed) if wave<=2018 & pais==3, at(0,1,7,13,30) icode
{txt}(297,931 missing values generated)

{com}. egen pais4 = cut(ed) if wave<=2018 & pais==4, at(0,1,7,13,30) icode
{txt}(297,746 missing values generated)

{com}. egen pais5 = cut(ed) if wave<=2018 & pais==5, at(0,1,7,12,30) icode  
{txt}(297,717 missing values generated)

{com}. egen pais6 = cut(ed) if wave<=2018 & pais==6, at(0,1,7,13,30) icode
{txt}(298,281 missing values generated)

{com}. egen pais7 = cut(ed) if wave<=2018 & pais==7, at(0,1,7,13,30) icode
{txt}(297,897 missing values generated)

{com}. egen pais8 = cut(ed) if wave<=2018 & pais==8, at(0,1,6,12,30) icode
{txt}(298,141 missing values generated)

{com}. egen pais9 = cut(ed) if wave<=2018 & pais==9, at(0,1,7,13,30) icode
{txt}(292,350 missing values generated)

{com}. egen pais10 = cut(ed) if wave<=2018 & pais==10, at(0,1,7,13,30) icode
{txt}(288,732 missing values generated)

{com}. egen pais11 = cut(ed) if wave<=2018 & pais==11, at(0,1,7,12,30) icode
{txt}(298,676 missing values generated)

{com}. egen pais12 = cut(ed) if wave<=2018 & pais==12, at(0,1,7,13,30) icode
{txt}(300,444 missing values generated)

{com}. egen pais13 = cut(ed) if wave<=2018 & pais==13, at(0,1,7,13,30) icode
{txt}(298,891 missing values generated)

{com}. egen pais14 = cut(ed) if wave<=2018 & pais==14, at(0,1,7,13,30) icode
{txt}(299,950 missing values generated)

{com}. egen pais15 = cut(ed) if wave<=2018 & pais==15, at(0,1,7,13,30) icode
{txt}(300,370 missing values generated)

{com}. egen pais16 = cut(ed) if wave<=2018 & pais==16, at(0,1,7,12,30) icode
{txt}(301,277 missing values generated)

{com}. egen pais17 = cut(ed) if wave<=2018 & pais==17, at(0,1,8,13,30) icode
{txt}(301,305 missing values generated)

{com}. egen pais21 = cut(ed) if wave<=2018 & pais==21, at(0,1,9,13,30) icode
{txt}(295,339 missing values generated)

{com}. egen pais22 = cut(ed) if wave<=2018 & pais==22, at(0,1,8,15,30) icode
{txt}(299,928 missing values generated)

{com}. egen pais23 = cut(ed) if wave<=2018 & pais==23, at(0,1,7,12,30) icode
{txt}(300,120 missing values generated)

{com}. egen pais24 = cut(ed) if wave<=2018 & pais==24, at(0,1,7,12,30) icode
{txt}(300,117 missing values generated)

{com}. egen pais25 = cut(ed) if wave<=2018 & pais==25, at(0,1,6,13,30) icode
{txt}(303,202 missing values generated)

{com}. egen pais26 = cut(ed) if wave<=2018 & pais==26, at(0,1,7,13,30) icode
{txt}(304,189 missing values generated)

{com}. egen pais27 = cut(ed) if wave<=2018 & pais==27, at(0,1,7,14,30) icode
{txt}(303,269 missing values generated)

{com}. egen pais28 = cut(ed) if wave<=2018 & pais==27, at(0,1,7,13,30) icode
{txt}(303,269 missing values generated)

{com}. egen pais29 = cut(ed) if wave<=2018 & pais==29, at(0,1,7,13,30) icode
{txt}(306,504 missing values generated)

{com}. egen pais30 = cut(ed) if wave<=2018 & pais==30, at(0,1,8,13,30) icode
{txt}(309,279 missing values generated)

{com}. egen pais31 = cut(ed) if wave<=2018 & pais==31, at(0,1,8,13,30) icode
{txt}(309,248 missing values generated)

{com}. egen pais32 = cut(ed) if wave<=2018 & pais==32, at(0,1,8,13,30) icode
{txt}(309,250 missing values generated)

{com}. egen pais33 = cut(ed) if wave<=2018 & pais==33, at(0,1,8,13,30) icode
{txt}(310,254 missing values generated)

{com}. egen pais34 = cut(ed) if wave<=2018 & pais==34, at(0,1,8,13,30) icode
{txt}(309,250 missing values generated)

{com}. egen pais35 = cut(ed) if wave<=2018 & pais==35, at(0,1,8,13,30) icode
{txt}(309,266 missing values generated)

{com}.  
. forval i=1/35 {c -(}
{txt}  2{com}.       cap replace edr=pais`i' if pais==`i' & wave<=2018
{txt}  3{com}.       cap drop pais`i'
{txt}  4{com}. {c )-}
{txt}
{com}. 
. 
. 
. **  creating quintall (levels of wealth) 
. cap ssc install sumdist
{txt}
{com}. gen quintall=.
{txt}(310,254 missing values generated)

{com}. levelsof wave , local(W)
{res}{txt}2004 2006 2008 2010 2012 2014 2016 2018

{com}. cap recode r16 (.c=0), gen(tempr16)
{txt}
{com}. foreach w of local W {c -(}
{txt}  2{com}.     if `w'==2004 levelsof pais if wave==`w' & inrange(pais, 1, 10), local(K)
{txt}  3{com}.     if `w'==2006 levelsof pais if wave==`w' & inrange(pais, 1, 13) | pais==16 | inrange(pais, 18, 24), local(K)
{txt}  4{com}.     if `w'==2008 levelsof pais if wave==`w' & inrange(pais, 1, 24) | pais==26 , local(K)
{txt}  5{com}.     if inrange(`w', 2010, 2018) levelsof pais if wave==`w' & pais<40, local(K)
{txt}  6{com}.     foreach k of local K {c -(}
{txt}  7{com}.         qui levelsof ur if pais==`k' & wave==`w', local(U)
{txt}  8{com}.         foreach u of local U {c -(}
{txt}  9{com}.             if `w'==2004 qui pca r1 r3 r4 r5 r6 r7 r12 if pais==`k' & ur==`u' & wave==`w'
{txt} 10{com}.             if inlist(`w', 2006, 2008) qui pca r1 r3 r4 r4a r5 r6 r7 r8 r12 r14 r15 if pais==`k' & ur==`u' & wave==`w'
{txt} 11{com}.             if `w'==2010 qui pca r1 r3 r4 r4a r5 r6 r7  r8 r12 r14 r15 r16 r18 if pais==`k' & ur==`u' & wave==`w'
{txt} 12{com}.             if `w'==2012 qui pca r1 r3 r4 r4a r5 r6 r7 r8 r12 r14 r15 r16 if pais==`k' & ur==`u' & wave==`w'
{txt} 13{com}.             if inrange(`w', 2014, 2018) qui pca r1 r3 r4 r4a r5 r6 r7 r8 r12 r14 r15 tempr16 r18 if pais==`k'& ur==`u' & wave==`w'
{txt} 14{com}.             qui predict _f1 if pais==`k' & ur==`u' & wave==`w'
{txt} 15{com}.             qui sumdist _f1 if !mi(_f1), n(5) qgp(_quint)
{txt} 16{com}.             qui replace quintall=_quint if pais==`k' & ur==`u' & wave==`w'
{txt} 17{com}.             drop _quint _f1
{txt} 18{com}.         {c )-}
{txt} 19{com}.     {c )-}
{txt} 20{com}. {c )-}
{res}{txt}1 2 3 4 5 6 7 8 9 10
{res}{txt}1 2 3 4 5 6 7 8 9 10 11 12 13 16 21 22 23 24
{res}{txt}1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 21 22 23 24 26
{res}{txt}1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 21 22 23 24 25 26 27
{res}{txt}1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 21 22 23 24 25 26 27
{res}{txt}1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 21 22 23 24 25 26 27 28 29
{res}{txt}1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 21 22 23 24 30 31 32 34 35
{res}{txt}1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 17 21 23

{com}. cap drop tempr16
{txt}
{com}. 
{txt}end of do-file

{com}. save "AB_cps.dta", replace
{txt}{p 0 4 2}
(file {bf}
AB_cps.dta{rm}
not found)
{p_end}
{p 0 4 2}
file {bf}
AB_cps.dta{rm}
saved
{p_end}

{com}. 
. ****Vote buying exposure
. *2010 (code any exposure to 1, and code no exposure to 0)
. recode clien1 (1/2=1)(3=0), gen(client3)
{txt}(41,930 differences between {bf:clien1} and {bf:client3})

{com}. lab var client3 "Vote Buying Target"
{txt}
{com}. *2014 direct
. recode clien1na (2=0), gen(client)
{txt}(47,955 differences between {bf:clien1na} and {bf:client})

{com}. lab var client "Vote Buying Target (direct measure)"
{txt}
{com}. *2014 indirect
. recode clien1n (2=0), gen(indirect)
{txt}(39,880 differences between {bf:clien1n} and {bf:indirect})

{com}. lab var indirect "Vote Buying Target (indirect)"
{txt}
{com}. 
. *combining direct vote buying exposure to range across years
. tab client3 wave // 2010-2

      {txt}Vote {c |}
    Buying {c |}      Survey Wave
    Target {c |}      2010       2012 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}    32,464      5,726 {txt}{c |}{res}    38,190 
{txt}         1 {c |}{res}     4,487      1,634 {txt}{c |}{res}     6,121 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}    36,951      7,360 {txt}{c |}{res}    44,311 
{txt}
{com}.         tab client wave // 2014~

      {txt}Vote {c |}
    Buying {c |}
    Target {c |}
   (direct {c |}           Survey Wave
  measure) {c |}      2014    2016/17    2018/19 {c |}     Total
{hline 11}{c +}{hline 33}{c +}{hline 10}
         0 {c |}{res}    31,723      4,185     12,047 {txt}{c |}{res}    47,955 
{txt}         1 {c |}{res}     2,826        435      1,898 {txt}{c |}{res}     5,159 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
     Total {c |}{res}    34,549      4,620     13,945 {txt}{c |}{res}    53,114 
{txt}
{com}.         tab indirect wave // same

      {txt}Vote {c |}
    Buying {c |}
    Target {c |}           Survey Wave
(indirect) {c |}      2014    2016/17    2018/19 {c |}     Total
{hline 11}{c +}{hline 33}{c +}{hline 10}
         0 {c |}{res}    29,778      1,064      9,038 {txt}{c |}{res}    39,880 
{txt}         1 {c |}{res}     4,492        413      3,110 {txt}{c |}{res}     8,015 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
     Total {c |}{res}    34,270      1,477     12,148 {txt}{c |}{res}    47,895 
{txt}
{com}. egen direct = rowtotal(client3 client)
{txt}
{com}. replace direct=. if client>1 & client3>1
{txt}(212,829 real changes made, 212,829 to missing)

{com}. lab var direct "Vote Buying Target (direct)"
{txt}
{com}. 
. ***combining trust in elections to range across years
. tab b47a b47 // no obs
{txt}no observations

{com}.         tab b47a wave // from 2012, other than ecuador

                      {txt}{c |}                      Survey Wave
   Trust in Elections {c |}      2004       2012       2014    2016/17    2018/19 {c |}     Total
{hline 22}{c +}{hline 55}{c +}{hline 10}
           Not at All {c |}{res}       181      4,504      6,625      7,035      5,964 {txt}{c |}{res}    24,309 
{txt}                    2 {c |}{res}       180      3,254      3,575      3,481      3,211 {txt}{c |}{res}    13,701 
{txt}                    3 {c |}{res}       304      5,415      5,439      4,511      3,988 {txt}{c |}{res}    19,657 
{txt}                    4 {c |}{res}       508      7,422      7,055      5,820      5,326 {txt}{c |}{res}    26,131 
{txt}                    5 {c |}{res}       662      7,882      6,370      5,879      5,165 {txt}{c |}{res}    25,958 
{txt}                    6 {c |}{res}       629      5,041      4,410      4,489      3,752 {txt}{c |}{res}    18,321 
{txt}                A Lot {c |}{res}       513      4,099      3,896      3,861      3,192 {txt}{c |}{res}    15,561 
{txt}{hline 22}{c +}{hline 55}{c +}{hline 10}
                Total {c |}{res}     2,977     37,617     37,370     35,076     30,598 {txt}{c |}{res}   143,638 
{txt}
{com}.         tab b47 wave // ~2010

     {txt}�Hasta qu� punto {c |}
tiene usted confianza {c |}                 Survey Wave
   en las elecciones? {c |}      2004       2006       2008       2010 {c |}     Total
{hline 22}{c +}{hline 44}{c +}{hline 10}
               1 Nada {c |}{res}     1,756      4,202      5,329      5,078 {txt}{c |}{res}    16,365 
{txt}                    2 {c |}{res}     1,041      2,273      3,369      3,471 {txt}{c |}{res}    10,154 
{txt}                    3 {c |}{res}     1,217      3,078      5,204      5,466 {txt}{c |}{res}    14,965 
{txt}                    4 {c |}{res}     1,618      3,859      7,337      7,301 {txt}{c |}{res}    20,115 
{txt}                    5 {c |}{res}     2,104      4,014      6,760      7,458 {txt}{c |}{res}    20,336 
{txt}                    6 {c |}{res}     2,138      3,002      4,812      5,257 {txt}{c |}{res}    15,209 
{txt}              7 Mucho {c |}{res}     2,060      2,658      4,259      4,389 {txt}{c |}{res}    13,366 
{txt}{hline 22}{c +}{hline 44}{c +}{hline 10}
                Total {c |}{res}    11,934     23,086     37,070     38,420 {txt}{c |}{res}   110,510 
{txt}
{com}. egen trustel = rowtotal(b47a b47)
{txt}
{com}.         replace trustel=. if missing(b47) & missing(b47a)
{txt}(56,106 real changes made, 56,106 to missing)

{com}.         tab trustel wave

           {txt}{c |}                                       Survey Wave
   trustel {c |}      2004       2006       2008       2010       2012       2014    2016/17    2018/19 {c |}     Total
{hline 11}{c +}{hline 88}{c +}{hline 10}
         1 {c |}{res}     1,937      4,202      5,329      5,078      4,504      6,625      7,035      5,964 {txt}{c |}{res}    40,674 
{txt}         2 {c |}{res}     1,221      2,273      3,369      3,471      3,254      3,575      3,481      3,211 {txt}{c |}{res}    23,855 
{txt}         3 {c |}{res}     1,521      3,078      5,204      5,466      5,415      5,439      4,511      3,988 {txt}{c |}{res}    34,622 
{txt}         4 {c |}{res}     2,126      3,859      7,337      7,301      7,422      7,055      5,820      5,326 {txt}{c |}{res}    46,246 
{txt}         5 {c |}{res}     2,766      4,014      6,760      7,458      7,882      6,370      5,879      5,165 {txt}{c |}{res}    46,294 
{txt}         6 {c |}{res}     2,767      3,002      4,812      5,257      5,041      4,410      4,489      3,752 {txt}{c |}{res}    33,530 
{txt}         7 {c |}{res}     2,573      2,658      4,259      4,389      4,099      3,896      3,861      3,192 {txt}{c |}{res}    28,927 
{txt}{hline 11}{c +}{hline 88}{c +}{hline 10}
     Total {c |}{res}    14,911     23,086     37,070     38,420     37,617     37,370     35,076     30,598 {txt}{c |}{res}   254,148 
{txt}
{com}.         
. ***approval of vote buying (2018 data only)
. egen approval=rowtotal(clien4 clien4a clien4b)
{txt}
{com}.         replace approval=. if missing(clien4) & missing(clien4a) & missing(clien4b)
{txt}(299,826 real changes made, 299,826 to missing)

{com}.         rescale approval 1 0 // to run from strongly disagree(0) to strongly agree (1)
{txt}(9,828 real changes made)

{com}. 
. ****controls
. rescale quintall 0 1
{txt}(281,230 real changes made)

{com}. rescale edr 0 1
{txt}(272,625 real changes made)

{com}. rescale m1 1 0
{txt}variable {bf}{res}m1{sf}{txt} was {bf}{res}byte{sf}{txt} now {bf}{res}float{sf}
{txt}(286,834 real changes made, 15,515 to missing)

{com}. recode q1 (1=0)(2=1)(3=0), gen(woman)
{txt}(310,222 differences between {bf:q1} and {bf:woman})

{com}.         tab woman q1, mis

 {txt}RECODE of {c |}                                     Sex
  q1 (Sex) {c |}      Male     Female      Other          .  Don't Kno  No Respon  Not Appli {c |}     Total
{hline 11}{c +}{hline 77}{c +}{hline 10}
         0 {c |}{res}   151,838          0         11          0          0          0          0 {txt}{c |}{res}   151,849 
{txt}         1 {c |}{res}         0    158,373          0          0          0          0          0 {txt}{c |}{res}   158,373 
{txt}         . {c |}{res}         0          0          0         15          0          0          0 {txt}{c |}{res}        15 
{txt}        .a {c |}{res}         0          0          0          0          3          0          0 {txt}{c |}{res}         3 
{txt}        .b {c |}{res}         0          0          0          0          0         13          0 {txt}{c |}{res}        13 
{txt}        .c {c |}{res}         0          0          0          0          0          0          1 {txt}{c |}{res}         1 
{txt}{hline 11}{c +}{hline 77}{c +}{hline 10}
     Total {c |}{res}   151,838    158,373         11         15          3         13          1 {txt}{c |}{res}   310,254 
{txt}
{com}. recode q2 (16/25=0)(26/35=1)(36/45=2)(46/55=3)(56/65=4)(66/112=5), gen(agecohort)
{txt}(307,627 differences between {bf:q2} and {bf:agecohort})

{com}.         rescale agecohort 0 1
{txt}variable {bf}{res}agecohort{sf}{txt} was {bf}{res}int{sf}{txt} now {bf}{res}float{sf}
{txt}(240,303 real changes made, 2,610 to missing)

{com}.         lab def agecohort 0 "16-25" 1 "66+"
{txt}
{com}.         lab val agecohort agecohort
{txt}
{com}.         fre agecohort
{res}
{txt}agecohort {hline 2} RECODE of q2 (Age)
{txt}{hline 16}{hline 1}{c TT}{hline 44}
{txt}        {txt}         {c |}      Freq.    Percent      Valid       Cum.
{txt}{hline 16}{hline 1}{c +}{hline 44}
{txt}Valid   0  16-25 {c |}{res}      69934      22.54      22.73      22.73
{txt}        .2       {c |}{res}      72139      23.25      23.45      46.18
{txt}        .4       {c |}{res}      60842      19.61      19.78      65.96
{txt}        .6       {c |}{res}      47309      15.25      15.38      81.34
{txt}        .8       {c |}{res}      32479      10.47      10.56      91.90
{txt}        1  66+   {c |}{res}      24924       8.03       8.10     100.00
{txt}        Total    {c |}{res}     307627      99.15     100.00           
{txt}Missing .        {c |}{res}       2627       0.85                      
{txt}Total            {c |}{res}     310254     100.00                      
{txt}{hline 16}{hline 1}{c BT}{hline 44}

{com}. 
. *ur is coded so that 1=urban, 2=rural*
. recode ur 1=0 2=1, gen(rural)
{txt}(290,476 differences between {bf:ur} and {bf:rural})

{com}. 
. *** labels
. lab var indirect "Indirect Exposure"
{txt}
{com}. lab var direct "Direct Exposure"
{txt}
{com}. lab var approval "Approval of Vote Buying"
{txt}
{com}. lab var rural "Rural"
{txt}
{com}. lab var woman "Woman"
{txt}
{com}. lab var edr "Education"
{txt}
{com}. lab var quintall "Wealth"
{txt}
{com}. 
. ***************************************************************** Main text analyses 
. cd "../figures and tables"
{res}C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables
{txt}
{com}. 
. *** Table 1
. *unmatched
. svy: reg trustel indirect woman quintall agecohort edr rural m1 i.pais if wave==2014  
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:118}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:32,507}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:1,728}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:30,670.472}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,610}
{txt}{col 51}{lalign 15:F({res:28}, {res:1583})}{col 66} = {res}{ralign 10:214.49}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.2087}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}            trustel{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}indirect {c |}{col 21}{res}{space 2}-.1479267{col 33}{space 2} .0332019{col 44}{space 1}   -4.46{col 53}{space 3}0.000{col 61}{space 4}-.2130502{col 74}{space 3}-.0828033
{txt}{space 14}woman {c |}{col 21}{res}{space 2}-.0665194{col 33}{space 2} .0193507{col 44}{space 1}   -3.44{col 53}{space 3}0.001{col 61}{space 4}-.1044747{col 74}{space 3}-.0285642
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}-.0326387{col 33}{space 2} .0341127{col 44}{space 1}   -0.96{col 53}{space 3}0.339{col 61}{space 4}-.0995487{col 74}{space 3} .0342714
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2} .2821528{col 33}{space 2}  .034513{col 44}{space 1}    8.18{col 53}{space 3}0.000{col 61}{space 4} .2144576{col 74}{space 3} .3498479
{txt}{space 16}edr {c |}{col 21}{res}{space 2} .1193252{col 33}{space 2} .0505881{col 44}{space 1}    2.36{col 53}{space 3}0.018{col 61}{space 4} .0200996{col 74}{space 3} .2185507
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .1819852{col 33}{space 2} .0291662{col 44}{space 1}    6.24{col 53}{space 3}0.000{col 61}{space 4} .1247775{col 74}{space 3} .2391929
{txt}{space 17}m1 {c |}{col 21}{res}{space 2} 2.587417{col 33}{space 2} .0490837{col 44}{space 1}   52.71{col 53}{space 3}0.000{col 61}{space 4} 2.491142{col 74}{space 3} 2.683692
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2}-.2465958{col 33}{space 2} .0886939{col 44}{space 1}   -2.78{col 53}{space 3}0.005{col 61}{space 4}-.4205634{col 74}{space 3}-.0726282
{txt}{space 7}El Salvador  {c |}{col 21}{res}{space 2} .3036904{col 33}{space 2} .0803992{col 44}{space 1}    3.78{col 53}{space 3}0.000{col 61}{space 4} .1459923{col 74}{space 3} .4613885
{txt}{space 10}Honduras  {c |}{col 21}{res}{space 2}-.4012645{col 33}{space 2} .0822822{col 44}{space 1}   -4.88{col 53}{space 3}0.000{col 61}{space 4} -.562656{col 74}{space 3}-.2398729
{txt}{space 9}Nicaragua  {c |}{col 21}{res}{space 2} .1166636{col 33}{space 2} .0862659{col 44}{space 1}    1.35{col 53}{space 3}0.176{col 61}{space 4}-.0525417{col 74}{space 3}  .285869
{txt}{space 8}Costa Rica  {c |}{col 21}{res}{space 2} 1.299849{col 33}{space 2} .0926828{col 44}{space 1}   14.02{col 53}{space 3}0.000{col 61}{space 4} 1.118057{col 74}{space 3} 1.481641
{txt}{space 12}Panama  {c |}{col 21}{res}{space 2} .2485693{col 33}{space 2} .0960026{col 44}{space 1}    2.59{col 53}{space 3}0.010{col 61}{space 4} .0602661{col 74}{space 3} .4368726
{txt}{space 10}Colombia  {c |}{col 21}{res}{space 2}-.5021815{col 33}{space 2} .0852057{col 44}{space 1}   -5.89{col 53}{space 3}0.000{col 61}{space 4}-.6693073{col 74}{space 3}-.3350557
{txt}{space 11}Ecuador  {c |}{col 21}{res}{space 2} .4880664{col 33}{space 2} .0879254{col 44}{space 1}    5.55{col 53}{space 3}0.000{col 61}{space 4} .3156061{col 74}{space 3} .6605268
{txt}{space 11}Bolivia  {c |}{col 21}{res}{space 2} .0615623{col 33}{space 2}   .07698{col 44}{space 1}    0.80{col 53}{space 3}0.424{col 61}{space 4}-.0894292{col 74}{space 3} .2125537
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2} .2650692{col 33}{space 2} .0893564{col 44}{space 1}    2.97{col 53}{space 3}0.003{col 61}{space 4} .0898021{col 74}{space 3} .4403364
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2} .1020364{col 33}{space 2} .0858501{col 44}{space 1}    1.19{col 53}{space 3}0.235{col 61}{space 4}-.0663534{col 74}{space 3} .2704262
{txt}{space 13}Chile  {c |}{col 21}{res}{space 2} .9375152{col 33}{space 2} .0992364{col 44}{space 1}    9.45{col 53}{space 3}0.000{col 61}{space 4} .7428691{col 74}{space 3} 1.132161
{txt}{space 11}Uruguay  {c |}{col 21}{res}{space 2} 1.657209{col 33}{space 2} .0847538{col 44}{space 1}   19.55{col 53}{space 3}0.000{col 61}{space 4} 1.490969{col 74}{space 3} 1.823448
{txt}{space 12}Brazil  {c |}{col 21}{res}{space 2}-.5686703{col 33}{space 2}  .087607{col 44}{space 1}   -6.49{col 53}{space 3}0.000{col 61}{space 4}-.7405061{col 74}{space 3}-.3968346
{txt}{space 9}Venezuela  {c |}{col 21}{res}{space 2} .5080928{col 33}{space 2} .0978544{col 44}{space 1}    5.19{col 53}{space 3}0.000{col 61}{space 4} .3161573{col 74}{space 3} .7000283
{txt}{space 9}Argentina  {c |}{col 21}{res}{space 2} .9769863{col 33}{space 2} .0927664{col 44}{space 1}   10.53{col 53}{space 3}0.000{col 61}{space 4} .7950307{col 74}{space 3} 1.158942
{txt}Dominican Republic  {c |}{col 21}{res}{space 2}-.5546879{col 33}{space 2} .0886151{col 44}{space 1}   -6.26{col 53}{space 3}0.000{col 61}{space 4}-.7285009{col 74}{space 3}-.3808748
{txt}{space 13}Haiti  {c |}{col 21}{res}{space 2}-1.254512{col 33}{space 2} .0949544{col 44}{space 1}  -13.21{col 53}{space 3}0.000{col 61}{space 4}-1.440759{col 74}{space 3}-1.068265
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2}-.2688843{col 33}{space 2} .0774852{col 44}{space 1}   -3.47{col 53}{space 3}0.001{col 61}{space 4}-.4208668{col 74}{space 3}-.1169018
{txt}{space 12}Guyana  {c |}{col 21}{res}{space 2}-.1366283{col 33}{space 2}   .10512{col 44}{space 1}   -1.30{col 53}{space 3}0.194{col 61}{space 4}-.3428147{col 74}{space 3} .0695581
{txt}{space 12}Belize  {c |}{col 21}{res}{space 2}  .180907{col 33}{space 2} .0811486{col 44}{space 1}    2.23{col 53}{space 3}0.026{col 61}{space 4}  .021739{col 74}{space 3} .3400749
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} 2.071229{col 33}{space 2}  .077442{col 44}{space 1}   26.75{col 53}{space 3}0.000{col 61}{space 4} 1.919331{col 74}{space 3} 2.223126
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using table1.doc, dec(2) drop (i.pais) replace ctitle(Indirect, unmatched)
{txt}{stata `"shellout using `"table1.doc"'"':table1.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "table1.txt""':seeout}

{com}. svy: reg trustel direct woman quintall agecohort edr rural m1 i.pais if wave==2014  
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:118}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:32,745}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:1,728}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:30,901.257}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,610}
{txt}{col 51}{lalign 15:F({res:28}, {res:1583})}{col 66} = {res}{ralign 10:215.68}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.2090}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}            trustel{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}direct {c |}{col 21}{res}{space 2} -.155367{col 33}{space 2} .0390183{col 44}{space 1}   -3.98{col 53}{space 3}0.000{col 61}{space 4} -.231899{col 74}{space 3}-.0788349
{txt}{space 14}woman {c |}{col 21}{res}{space 2}-.0654955{col 33}{space 2} .0191581{col 44}{space 1}   -3.42{col 53}{space 3}0.001{col 61}{space 4} -.103073{col 74}{space 3}-.0279181
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}-.0255263{col 33}{space 2} .0341299{col 44}{space 1}   -0.75{col 53}{space 3}0.455{col 61}{space 4}  -.09247{col 74}{space 3} .0414174
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2} .2836778{col 33}{space 2} .0344945{col 44}{space 1}    8.22{col 53}{space 3}0.000{col 61}{space 4} .2160189{col 74}{space 3} .3513367
{txt}{space 16}edr {c |}{col 21}{res}{space 2} .1099976{col 33}{space 2} .0504825{col 44}{space 1}    2.18{col 53}{space 3}0.029{col 61}{space 4} .0109793{col 74}{space 3} .2090158
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .1804977{col 33}{space 2} .0290918{col 44}{space 1}    6.20{col 53}{space 3}0.000{col 61}{space 4} .1234359{col 74}{space 3} .2375594
{txt}{space 17}m1 {c |}{col 21}{res}{space 2} 2.584102{col 33}{space 2} .0491513{col 44}{space 1}   52.57{col 53}{space 3}0.000{col 61}{space 4} 2.487695{col 74}{space 3}  2.68051
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2}-.2375117{col 33}{space 2} .0899625{col 44}{space 1}   -2.64{col 53}{space 3}0.008{col 61}{space 4}-.4139676{col 74}{space 3}-.0610557
{txt}{space 7}El Salvador  {c |}{col 21}{res}{space 2} .3022005{col 33}{space 2} .0800113{col 44}{space 1}    3.78{col 53}{space 3}0.000{col 61}{space 4} .1452633{col 74}{space 3} .4591377
{txt}{space 10}Honduras  {c |}{col 21}{res}{space 2}-.3998069{col 33}{space 2}  .081949{col 44}{space 1}   -4.88{col 53}{space 3}0.000{col 61}{space 4}-.5605449{col 74}{space 3} -.239069
{txt}{space 9}Nicaragua  {c |}{col 21}{res}{space 2}  .121049{col 33}{space 2}    .0859{col 44}{space 1}    1.41{col 53}{space 3}0.159{col 61}{space 4}-.0474385{col 74}{space 3} .2895365
{txt}{space 8}Costa Rica  {c |}{col 21}{res}{space 2} 1.311823{col 33}{space 2} .0907892{col 44}{space 1}   14.45{col 53}{space 3}0.000{col 61}{space 4} 1.133745{col 74}{space 3}   1.4899
{txt}{space 12}Panama  {c |}{col 21}{res}{space 2} .2557433{col 33}{space 2} .0954494{col 44}{space 1}    2.68{col 53}{space 3}0.007{col 61}{space 4} .0685252{col 74}{space 3} .4429614
{txt}{space 10}Colombia  {c |}{col 21}{res}{space 2}-.5077813{col 33}{space 2} .0850868{col 44}{space 1}   -5.97{col 53}{space 3}0.000{col 61}{space 4}-.6746739{col 74}{space 3}-.3408888
{txt}{space 11}Ecuador  {c |}{col 21}{res}{space 2} .4938903{col 33}{space 2}  .087911{col 44}{space 1}    5.62{col 53}{space 3}0.000{col 61}{space 4} .3214583{col 74}{space 3} .6663223
{txt}{space 11}Bolivia  {c |}{col 21}{res}{space 2}  .062544{col 33}{space 2} .0769966{col 44}{space 1}    0.81{col 53}{space 3}0.417{col 61}{space 4}-.0884801{col 74}{space 3} .2135681
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2} .2664496{col 33}{space 2} .0894576{col 44}{space 1}    2.98{col 53}{space 3}0.003{col 61}{space 4}  .090984{col 74}{space 3} .4419151
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2} .1030296{col 33}{space 2} .0850085{col 44}{space 1}    1.21{col 53}{space 3}0.226{col 61}{space 4}-.0637093{col 74}{space 3} .2697684
{txt}{space 13}Chile  {c |}{col 21}{res}{space 2} .9392075{col 33}{space 2} .0985688{col 44}{space 1}    9.53{col 53}{space 3}0.000{col 61}{space 4} .7458708{col 74}{space 3} 1.132544
{txt}{space 11}Uruguay  {c |}{col 21}{res}{space 2} 1.654373{col 33}{space 2} .0844439{col 44}{space 1}   19.59{col 53}{space 3}0.000{col 61}{space 4} 1.488742{col 74}{space 3} 1.820005
{txt}{space 12}Brazil  {c |}{col 21}{res}{space 2}-.5766152{col 33}{space 2}  .087536{col 44}{space 1}   -6.59{col 53}{space 3}0.000{col 61}{space 4}-.7483118{col 74}{space 3}-.4049187
{txt}{space 9}Venezuela  {c |}{col 21}{res}{space 2} .5173236{col 33}{space 2} .0976508{col 44}{space 1}    5.30{col 53}{space 3}0.000{col 61}{space 4} .3257875{col 74}{space 3} .7088597
{txt}{space 9}Argentina  {c |}{col 21}{res}{space 2} .9682113{col 33}{space 2} .0930207{col 44}{space 1}   10.41{col 53}{space 3}0.000{col 61}{space 4}  .785757{col 74}{space 3} 1.150666
{txt}Dominican Republic  {c |}{col 21}{res}{space 2}-.5668383{col 33}{space 2} .0890365{col 44}{space 1}   -6.37{col 53}{space 3}0.000{col 61}{space 4} -.741478{col 74}{space 3}-.3921986
{txt}{space 13}Haiti  {c |}{col 21}{res}{space 2}-1.268174{col 33}{space 2} .0954716{col 44}{space 1}  -13.28{col 53}{space 3}0.000{col 61}{space 4}-1.455436{col 74}{space 3}-1.080912
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2}-.2857086{col 33}{space 2} .0782729{col 44}{space 1}   -3.65{col 53}{space 3}0.000{col 61}{space 4}-.4392361{col 74}{space 3}-.1321812
{txt}{space 12}Guyana  {c |}{col 21}{res}{space 2}-.1355178{col 33}{space 2} .1046532{col 44}{space 1}   -1.29{col 53}{space 3}0.196{col 61}{space 4}-.3407886{col 74}{space 3}  .069753
{txt}{space 12}Belize  {c |}{col 21}{res}{space 2} .1662796{col 33}{space 2} .0805933{col 44}{space 1}    2.06{col 53}{space 3}0.039{col 61}{space 4} .0082009{col 74}{space 3} .3243584
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}  2.06674{col 33}{space 2} .0770197{col 44}{space 1}   26.83{col 53}{space 3}0.000{col 61}{space 4} 1.915671{col 74}{space 3}  2.21781
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using table1.doc, dec(2) drop (i.pais) append ctitle(Direct, unmatched)
{txt}{stata `"shellout using `"table1.doc"'"':table1.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "table1.txt""':seeout}

{com}. *matched
. ebalance indirect woman quintall agecohort edr rural i.pais if wave==2014, tar(3) gen(indirectwt)
{txt}note: 1b.pais omitted because of collinearity
{res}

Data Setup
{txt}Treatment variable:   {res}indirect
{txt}Covariate adjustment:{res} woman quintall agecohort edr rural 2.pais 3.pais 4.pais 5.pais 6.pais 7.pais 8.pais 9.pais 10.pais 11.pais 12.pais 13.pais 14.pais 15.pais 16.pais 17.pais 21.pais 22.pais 23.pais 24.pais 26.pais {txt}(1st order).{res} woman quintall agecohort edr rural 2.pais 3.pais 4.pais 5.pais 6.pais 7.pais 8.pais 9.pais 10.pais 11.pais 12.pais 13.pais 14.pais 15.pais 16.pais 17.pais 21.pais 22.pais 23.pais 24.pais 26.pais {txt}(2nd order).{res}{res} woman quintall agecohort edr rural 2.pais 3.pais 4.pais 5.pais 6.pais 7.pais 8.pais 9.pais 10.pais 11.pais 12.pais 13.pais 14.pais 15.pais 16.pais 17.pais 21.pais 22.pais 23.pais 24.pais 26.pais {txt}(3rd order).


{res}Optimizing...
{txt}Iteration 1: Max Difference = {res}142899.07{txt}
{txt}Iteration 2: Max Difference = {res}52569.3841{txt}
{txt}Iteration 3: Max Difference = {res}19338.9499{txt}
{txt}Iteration 4: Max Difference = {res}7114.15632{txt}
{txt}Iteration 5: Max Difference = {res}2616.90613{txt}
{txt}Iteration 6: Max Difference = {res}962.460351{txt}
{txt}Iteration 7: Max Difference = {res}353.824043{txt}
{txt}Iteration 8: Max Difference = {res}129.920014{txt}
{txt}Iteration 9: Max Difference = {res}47.5523029{txt}
{txt}Iteration 10: Max Difference = {res}17.2675737{txt}
{txt}Iteration 11: Max Difference = {res}6.14583471{txt}
{txt}Iteration 12: Max Difference = {res}2.06770697{txt}
{txt}Iteration 13: Max Difference = {res}.591652257{txt}
{txt}Iteration 14: Max Difference = {res}.107368499{txt}
{txt}Iteration 15: Max Difference = {res}.005983245{txt}
{txt}maximum difference smaller than the tolerance level; {res}convergence achieved


Treated units: {txt}4407{col 24}{res}total of weights: {txt}4407
{res}Control units: {txt}29104{col 24}{res}total of weights: {txt}4407


{res}Before: {txt}without weighting
{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treat}{space 1}{c |}{space 1}{rcenter 31:Control}{space 1}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:mean}{space 1}{space 1}{ralign 9:variance}{space 1}{space 1}{ralign 9:skewness}{space 1}{c |}{space 1}{ralign 9:mean}{space 1}{space 1}{ralign 9:variance}{space 1}{space 1}{ralign 9:skewness}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 10}
{space 0}{space 0}{ralign 12:woman}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .4702}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2492}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1196}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     .519}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2496}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.0762}}}{space 1}
{space 0}{space 0}{ralign 12:quintall}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .4995}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1276}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.0211}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .4878}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1266}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03824}}}{space 1}
{space 0}{space 0}{ralign 12:agecohort}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3462}}}{space 1}{space 1}{ralign 9:{res:{sf:   .08108}}}{space 1}{space 1}{ralign 9:{res:{sf:    .5509}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .4024}}}{space 1}{space 1}{ralign 9:{res:{sf:   .09858}}}{space 1}{space 1}{ralign 9:{res:{sf:    .3812}}}{space 1}
{space 0}{space 0}{ralign 12:edr}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .6226}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06393}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.2587}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .6165}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06486}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.1747}}}{space 1}
{space 0}{space 0}{ralign 12:rural}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     .346}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2263}}}{space 1}{space 1}{ralign 9:{res:{sf:    .6473}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3194}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2174}}}{space 1}{space 1}{ralign 9:{res:{sf:    .7747}}}{space 1}
{space 0}{space 0}{ralign 12:2.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04652}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04436}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.307}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04226}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04048}}}{space 1}{space 1}{ralign 9:{res:{sf:     4.55}}}{space 1}
{space 0}{space 0}{ralign 12:3.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03835}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03689}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.808}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04535}}}{space 1}{space 1}{ralign 9:{res:{sf:    .0433}}}{space 1}{space 1}{ralign 9:{res:{sf:     4.37}}}{space 1}
{space 0}{space 0}{ralign 12:4.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .07556}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06987}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.212}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04085}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03919}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.639}}}{space 1}
{space 0}{space 0}{ralign 12:5.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03177}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03077}}}{space 1}{space 1}{ralign 9:{res:{sf:     5.34}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04776}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04548}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.241}}}{space 1}
{space 0}{space 0}{ralign 12:6.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  .008396}}}{space 1}{space 1}{ralign 9:{res:{sf:  .008327}}}{space 1}{space 1}{ralign 9:{res:{sf:    10.78}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04903}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04663}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.177}}}{space 1}
{space 0}{space 0}{ralign 12:7.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .02768}}}{space 1}{space 1}{ralign 9:{res:{sf:   .02692}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.758}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04577}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04367}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.347}}}{space 1}
{space 0}{space 0}{ralign 12:8.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04833}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04601}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.212}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04292}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04107}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.511}}}{space 1}
{space 0}{space 0}{ralign 12:9.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03199}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03098}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.319}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04377}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04186}}}{space 1}{space 1}{ralign 9:{res:{sf:     4.46}}}{space 1}
{space 0}{space 0}{ralign 12:10.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03789}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03647}}}{space 1}{space 1}{ralign 9:{res:{sf:     4.84}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .09555}}}{space 1}{space 1}{ralign 9:{res:{sf:   .08643}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.752}}}{space 1}
{space 0}{space 0}{ralign 12:11.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03018}}}{space 1}{space 1}{ralign 9:{res:{sf:   .02928}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.492}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04535}}}{space 1}{space 1}{ralign 9:{res:{sf:    .0433}}}{space 1}{space 1}{ralign 9:{res:{sf:     4.37}}}{space 1}
{space 0}{space 0}{ralign 12:12.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .07102}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06599}}}{space 1}{space 1}{ralign 9:{res:{sf:     3.34}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03879}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03729}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.777}}}{space 1}
{space 0}{space 0}{ralign 12:13.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  .009303}}}{space 1}{space 1}{ralign 9:{res:{sf:  .009219}}}{space 1}{space 1}{ralign 9:{res:{sf:    10.22}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .0491}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04669}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.174}}}{space 1}
{space 0}{space 0}{ralign 12:14.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03404}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03289}}}{space 1}{space 1}{ralign 9:{res:{sf:     5.14}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04628}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04414}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.319}}}{space 1}
{space 0}{space 0}{ralign 12:15.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .07284}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06755}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.287}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03986}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03827}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.704}}}{space 1}
{space 0}{space 0}{ralign 12:16.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01293}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01277}}}{space 1}{space 1}{ralign 9:{res:{sf:    8.621}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04773}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04545}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.243}}}{space 1}
{space 0}{space 0}{ralign 12:17.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01974}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01936}}}{space 1}{space 1}{ralign 9:{res:{sf:    6.905}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04494}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04292}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.393}}}{space 1}
{space 0}{space 0}{ralign 12:21.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .1062}}}{space 1}{space 1}{ralign 9:{res:{sf:   .09494}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.556}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03484}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03363}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.073}}}{space 1}
{space 0}{space 0}{ralign 12:22.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .06127}}}{space 1}{space 1}{ralign 9:{res:{sf:   .05753}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.659}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03285}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03177}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.242}}}{space 1}
{space 0}{space 0}{ralign 12:23.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04447}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04251}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.419}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04223}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04045}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.552}}}{space 1}
{space 0}{space 0}{ralign 12:24.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01793}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01761}}}{space 1}{space 1}{ralign 9:{res:{sf:    7.267}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     .049}}}{space 1}{space 1}{ralign 9:{res:{sf:    .0466}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.179}}}{space 1}
{space 0}{space 0}{ralign 12:26.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .09803}}}{space 1}{space 1}{ralign 9:{res:{sf:   .08844}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.704}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03663}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03529}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.934}}}{space 1}


{res}After:  {txt}indirectwt as the weighting variable
{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treat}{space 1}{c |}{space 1}{rcenter 31:Control}{space 1}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:mean}{space 1}{space 1}{ralign 9:variance}{space 1}{space 1}{ralign 9:skewness}{space 1}{c |}{space 1}{ralign 9:mean}{space 1}{space 1}{ralign 9:variance}{space 1}{space 1}{ralign 9:skewness}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 10}
{space 0}{space 0}{ralign 12:woman}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .4702}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2492}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1196}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .4702}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2491}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1195}}}{space 1}
{space 0}{space 0}{ralign 12:quintall}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .4995}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1276}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.0211}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .4995}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1276}}}{space 1}{space 1}{ralign 9:{res:{sf:  -.02106}}}{space 1}
{space 0}{space 0}{ralign 12:agecohort}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3462}}}{space 1}{space 1}{ralign 9:{res:{sf:   .08108}}}{space 1}{space 1}{ralign 9:{res:{sf:    .5509}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3462}}}{space 1}{space 1}{ralign 9:{res:{sf:   .08108}}}{space 1}{space 1}{ralign 9:{res:{sf:    .5509}}}{space 1}
{space 0}{space 0}{ralign 12:edr}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .6226}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06393}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.2587}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .6226}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06393}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.2586}}}{space 1}
{space 0}{space 0}{ralign 12:rural}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     .346}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2263}}}{space 1}{space 1}{ralign 9:{res:{sf:    .6473}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3461}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2263}}}{space 1}{space 1}{ralign 9:{res:{sf:    .6471}}}{space 1}
{space 0}{space 0}{ralign 12:2.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04652}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04436}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.307}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04652}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04436}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.307}}}{space 1}
{space 0}{space 0}{ralign 12:3.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03835}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03689}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.808}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03835}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03688}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.808}}}{space 1}
{space 0}{space 0}{ralign 12:4.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .07556}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06987}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.212}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .07556}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06986}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.212}}}{space 1}
{space 0}{space 0}{ralign 12:5.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03177}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03077}}}{space 1}{space 1}{ralign 9:{res:{sf:     5.34}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03177}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03076}}}{space 1}{space 1}{ralign 9:{res:{sf:     5.34}}}{space 1}
{space 0}{space 0}{ralign 12:6.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  .008396}}}{space 1}{space 1}{ralign 9:{res:{sf:  .008327}}}{space 1}{space 1}{ralign 9:{res:{sf:    10.78}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  .008454}}}{space 1}{space 1}{ralign 9:{res:{sf:  .008383}}}{space 1}{space 1}{ralign 9:{res:{sf:    10.74}}}{space 1}
{space 0}{space 0}{ralign 12:7.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .02768}}}{space 1}{space 1}{ralign 9:{res:{sf:   .02692}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.758}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .02768}}}{space 1}{space 1}{ralign 9:{res:{sf:   .02692}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.758}}}{space 1}
{space 0}{space 0}{ralign 12:8.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04833}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04601}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.212}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04833}}}{space 1}{space 1}{ralign 9:{res:{sf:     .046}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.212}}}{space 1}
{space 0}{space 0}{ralign 12:9.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03199}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03098}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.319}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     .032}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03097}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.319}}}{space 1}
{space 0}{space 0}{ralign 12:10.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03789}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03647}}}{space 1}{space 1}{ralign 9:{res:{sf:     4.84}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .0379}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03646}}}{space 1}{space 1}{ralign 9:{res:{sf:     4.84}}}{space 1}
{space 0}{space 0}{ralign 12:11.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03018}}}{space 1}{space 1}{ralign 9:{res:{sf:   .02928}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.492}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03018}}}{space 1}{space 1}{ralign 9:{res:{sf:   .02927}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.492}}}{space 1}
{space 0}{space 0}{ralign 12:12.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .07102}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06599}}}{space 1}{space 1}{ralign 9:{res:{sf:     3.34}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .07102}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06598}}}{space 1}{space 1}{ralign 9:{res:{sf:     3.34}}}{space 1}
{space 0}{space 0}{ralign 12:13.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  .009303}}}{space 1}{space 1}{ralign 9:{res:{sf:  .009219}}}{space 1}{space 1}{ralign 9:{res:{sf:    10.22}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  .009347}}}{space 1}{space 1}{ralign 9:{res:{sf:   .00926}}}{space 1}{space 1}{ralign 9:{res:{sf:     10.2}}}{space 1}
{space 0}{space 0}{ralign 12:14.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03404}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03289}}}{space 1}{space 1}{ralign 9:{res:{sf:     5.14}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03404}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03288}}}{space 1}{space 1}{ralign 9:{res:{sf:     5.14}}}{space 1}
{space 0}{space 0}{ralign 12:15.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .07284}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06755}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.287}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .07284}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06754}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.287}}}{space 1}
{space 0}{space 0}{ralign 12:16.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01293}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01277}}}{space 1}{space 1}{ralign 9:{res:{sf:    8.621}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01294}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01278}}}{space 1}{space 1}{ralign 9:{res:{sf:    8.618}}}{space 1}
{space 0}{space 0}{ralign 12:17.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01974}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01936}}}{space 1}{space 1}{ralign 9:{res:{sf:    6.905}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01974}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01935}}}{space 1}{space 1}{ralign 9:{res:{sf:    6.904}}}{space 1}
{space 0}{space 0}{ralign 12:21.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .1062}}}{space 1}{space 1}{ralign 9:{res:{sf:   .09494}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.556}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .1062}}}{space 1}{space 1}{ralign 9:{res:{sf:   .09493}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.556}}}{space 1}
{space 0}{space 0}{ralign 12:22.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .06127}}}{space 1}{space 1}{ralign 9:{res:{sf:   .05753}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.659}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .06127}}}{space 1}{space 1}{ralign 9:{res:{sf:   .05752}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.659}}}{space 1}
{space 0}{space 0}{ralign 12:23.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04447}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04251}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.419}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04448}}}{space 1}{space 1}{ralign 9:{res:{sf:    .0425}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.419}}}{space 1}
{space 0}{space 0}{ralign 12:24.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01793}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01761}}}{space 1}{space 1}{ralign 9:{res:{sf:    7.267}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01793}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01761}}}{space 1}{space 1}{ralign 9:{res:{sf:    7.266}}}{space 1}
{space 0}{space 0}{ralign 12:26.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .09803}}}{space 1}{space 1}{ralign 9:{res:{sf:   .08844}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.704}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .09803}}}{space 1}{space 1}{ralign 9:{res:{sf:   .08842}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.704}}}{space 1}
{res}{txt}
{com}. ebalance direct woman quintall agecohort edr rural i.pais if wave==2014, tar(3) gen(directwt)
{txt}note: 1b.pais omitted because of collinearity
{res}

Data Setup
{txt}Treatment variable:   {res}direct
{txt}Covariate adjustment:{res} woman quintall agecohort edr rural 2.pais 3.pais 4.pais 5.pais 6.pais 7.pais 8.pais 9.pais 10.pais 11.pais 12.pais 13.pais 14.pais 15.pais 16.pais 17.pais 21.pais 22.pais 23.pais 24.pais 26.pais {txt}(1st order).{res} woman quintall agecohort edr rural 2.pais 3.pais 4.pais 5.pais 6.pais 7.pais 8.pais 9.pais 10.pais 11.pais 12.pais 13.pais 14.pais 15.pais 16.pais 17.pais 21.pais 22.pais 23.pais 24.pais 26.pais {txt}(2nd order).{res}{res} woman quintall agecohort edr rural 2.pais 3.pais 4.pais 5.pais 6.pais 7.pais 8.pais 9.pais 10.pais 11.pais 12.pais 13.pais 14.pais 15.pais 16.pais 17.pais 21.pais 22.pais 23.pais 24.pais 26.pais {txt}(3rd order).


{res}Optimizing...
{txt}Iteration 1: Max Difference = {res}145799.136{txt}
{txt}Iteration 2: Max Difference = {res}53636.2763{txt}
{txt}Iteration 3: Max Difference = {res}19731.4551{txt}
{txt}Iteration 4: Max Difference = {res}7258.5684{txt}
{txt}Iteration 5: Max Difference = {res}2670.04985{txt}
{txt}Iteration 6: Max Difference = {res}982.028301{txt}
{txt}Iteration 7: Max Difference = {res}361.040104{txt}
{txt}Iteration 8: Max Difference = {res}132.59193{txt}
{txt}Iteration 9: Max Difference = {res}48.5521598{txt}
{txt}Iteration 10: Max Difference = {res}17.6396563{txt}
{txt}Iteration 11: Max Difference = {res}6.27615851{txt}
{txt}Iteration 12: Max Difference = {res}2.11010501{txt}
{txt}Iteration 13: Max Difference = {res}.603175827{txt}
{txt}Iteration 14: Max Difference = {res}.109081531{txt}
{txt}Iteration 15: Max Difference = {res}.006100976{txt}
{txt}maximum difference smaller than the tolerance level; {res}convergence achieved


Treated units: {txt}2777{col 24}{res}total of weights: {txt}2777
{res}Control units: {txt}31004{col 24}{res}total of weights: {txt}2777


{res}Before: {txt}without weighting
{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treat}{space 1}{c |}{space 1}{rcenter 31:Control}{space 1}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:mean}{space 1}{space 1}{ralign 9:variance}{space 1}{space 1}{ralign 9:skewness}{space 1}{c |}{space 1}{ralign 9:mean}{space 1}{space 1}{ralign 9:variance}{space 1}{space 1}{ralign 9:skewness}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 10}
{space 0}{space 0}{ralign 12:woman}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .4523}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2478}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1917}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .5184}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2497}}}{space 1}{space 1}{ralign 9:{res:{sf:  -.07359}}}{space 1}
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{res}After:  {txt}directwt as the weighting variable
{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treat}{space 1}{c |}{space 1}{rcenter 31:Control}{space 1}
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{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 10}
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{res}{txt}
{com}. reg trustel indirect woman quintall agecohort edr rural m1 i.pais if wave==2014 [iweight=indirectwt]

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     8,596
{txt}{hline 13}{c +}{hline 34}   F(28, 8567)     = {res}    64.10
{txt}       Model {c |} {res} 5613.61885        28  200.486387   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 26796.7163     8,567  3.12789964   {txt}R-squared       ={res}    0.1732
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1706
{txt}       Total {c |} {res} 32410.3351     8,595  3.77083596   {txt}Root MSE        =   {res} 1.7685

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            trustel{col 21}{c |} Coefficient{col 33}  Std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}indirect {c |}{col 21}{res}{space 2}-.1507421{col 33}{space 2} .0382113{col 44}{space 1}   -3.94{col 53}{space 3}0.000{col 61}{space 4}-.2256455{col 74}{space 3}-.0758387
{txt}{space 14}woman {c |}{col 21}{res}{space 2} -.080564{col 33}{space 2} .0383572{col 44}{space 1}   -2.10{col 53}{space 3}0.036{col 61}{space 4}-.1557534{col 74}{space 3}-.0053746
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}-.0950876{col 33}{space 2}  .058115{col 44}{space 1}   -1.64{col 53}{space 3}0.102{col 61}{space 4} -.209007{col 74}{space 3} .0188318
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2}  .249733{col 33}{space 2}  .071465{col 44}{space 1}    3.49{col 53}{space 3}0.000{col 61}{space 4} .1096443{col 74}{space 3} .3898217
{txt}{space 16}edr {c |}{col 21}{res}{space 2} .0521779{col 33}{space 2} .0917224{col 44}{space 1}    0.57{col 53}{space 3}0.569{col 61}{space 4}-.1276201{col 74}{space 3} .2319759
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .2206702{col 33}{space 2} .0435676{col 44}{space 1}    5.07{col 53}{space 3}0.000{col 61}{space 4} .1352673{col 74}{space 3} .3060731
{txt}{space 17}m1 {c |}{col 21}{res}{space 2} 2.412269{col 33}{space 2} .0806328{col 44}{space 1}   29.92{col 53}{space 3}0.000{col 61}{space 4} 2.254209{col 74}{space 3} 2.570329
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2}-.0278446{col 33}{space 2} .1142227{col 44}{space 1}   -0.24{col 53}{space 3}0.807{col 61}{space 4}-.2517486{col 74}{space 3} .1960593
{txt}{space 7}El Salvador  {c |}{col 21}{res}{space 2}  .484448{col 33}{space 2} .1208343{col 44}{space 1}    4.01{col 53}{space 3}0.000{col 61}{space 4} .2475837{col 74}{space 3} .7213123
{txt}{space 10}Honduras  {c |}{col 21}{res}{space 2} -.345735{col 33}{space 2} .1007926{col 44}{space 1}   -3.43{col 53}{space 3}0.001{col 61}{space 4}-.5433127{col 74}{space 3}-.1481573
{txt}{space 9}Nicaragua  {c |}{col 21}{res}{space 2} .2349564{col 33}{space 2} .1286845{col 44}{space 1}    1.83{col 53}{space 3}0.068{col 61}{space 4}-.0172961{col 74}{space 3}  .487209
{txt}{space 8}Costa Rica  {c |}{col 21}{res}{space 2} .9639234{col 33}{space 2} .2174454{col 44}{space 1}    4.43{col 53}{space 3}0.000{col 61}{space 4} .5376781{col 74}{space 3} 1.390169
{txt}{space 12}Panama  {c |}{col 21}{res}{space 2} .4076001{col 33}{space 2} .1352347{col 44}{space 1}    3.01{col 53}{space 3}0.003{col 61}{space 4} .1425076{col 74}{space 3} .6726926
{txt}{space 10}Colombia  {c |}{col 21}{res}{space 2}-.3386836{col 33}{space 2} .1113801{col 44}{space 1}   -3.04{col 53}{space 3}0.002{col 61}{space 4}-.5570155{col 74}{space 3}-.1203518
{txt}{space 11}Ecuador  {c |}{col 21}{res}{space 2}  .630175{col 33}{space 2} .1292597{col 44}{space 1}    4.88{col 53}{space 3}0.000{col 61}{space 4} .3767948{col 74}{space 3} .8835552
{txt}{space 11}Bolivia  {c |}{col 21}{res}{space 2} .1981349{col 33}{space 2} .1216539{col 44}{space 1}    1.63{col 53}{space 3}0.103{col 61}{space 4}-.0403361{col 74}{space 3} .4366059
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2} .3582925{col 33}{space 2} .1319297{col 44}{space 1}    2.72{col 53}{space 3}0.007{col 61}{space 4} .0996785{col 74}{space 3} .6169066
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2} .2996216{col 33}{space 2} .1019754{col 44}{space 1}    2.94{col 53}{space 3}0.003{col 61}{space 4} .0997252{col 74}{space 3} .4995179
{txt}{space 13}Chile  {c |}{col 21}{res}{space 2} 1.368633{col 33}{space 2} .2158429{col 44}{space 1}    6.34{col 53}{space 3}0.000{col 61}{space 4}  .945529{col 74}{space 3} 1.791737
{txt}{space 11}Uruguay  {c |}{col 21}{res}{space 2} 1.732875{col 33}{space 2} .1259861{col 44}{space 1}   13.75{col 53}{space 3}0.000{col 61}{space 4} 1.485912{col 74}{space 3} 1.979838
{txt}{space 12}Brazil  {c |}{col 21}{res}{space 2} -.355532{col 33}{space 2}  .099137{col 44}{space 1}   -3.59{col 53}{space 3}0.000{col 61}{space 4}-.5498643{col 74}{space 3}-.1611997
{txt}{space 9}Venezuela  {c |}{col 21}{res}{space 2} .6668514{col 33}{space 2}  .182299{col 44}{space 1}    3.66{col 53}{space 3}0.000{col 61}{space 4} .3095015{col 74}{space 3} 1.024201
{txt}{space 9}Argentina  {c |}{col 21}{res}{space 2} 1.051832{col 33}{space 2} .1525662{col 44}{space 1}    6.89{col 53}{space 3}0.000{col 61}{space 4} .7527658{col 74}{space 3} 1.350899
{txt}Dominican Republic  {c |}{col 21}{res}{space 2}-.4352927{col 33}{space 2}  .094317{col 44}{space 1}   -4.62{col 53}{space 3}0.000{col 61}{space 4}-.6201767{col 74}{space 3}-.2504088
{txt}{space 13}Haiti  {c |}{col 21}{res}{space 2}-.8924347{col 33}{space 2} .1072628{col 44}{space 1}   -8.32{col 53}{space 3}0.000{col 61}{space 4}-1.102696{col 74}{space 3}-.6821738
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2}-.1863261{col 33}{space 2} .1149246{col 44}{space 1}   -1.62{col 53}{space 3}0.105{col 61}{space 4}-.4116059{col 74}{space 3} .0389538
{txt}{space 12}Guyana  {c |}{col 21}{res}{space 2} .0746335{col 33}{space 2} .1602279{col 44}{space 1}    0.47{col 53}{space 3}0.641{col 61}{space 4}-.2394517{col 74}{space 3} .3887188
{txt}{space 12}Belize  {c |}{col 21}{res}{space 2}  .291402{col 33}{space 2} .0938607{col 44}{space 1}    3.10{col 53}{space 3}0.002{col 61}{space 4} .1074125{col 74}{space 3} .4753915
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} 2.117051{col 33}{space 2}   .10848{col 44}{space 1}   19.52{col 53}{space 3}0.000{col 61}{space 4} 1.904405{col 74}{space 3} 2.329698
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using table1.doc, dec(2) append ctitle(Indirect, matched) drop(i.pais)
{txt}{stata `"shellout using `"table1.doc"'"':table1.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "table1.txt""':seeout}

{com}. reg trustel direct woman quintall agecohort edr rural m1 i.pais if wave==2014 [iweight=directwt]

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     5,417
{txt}{hline 13}{c +}{hline 34}   F(28, 5388)     = {res}    36.73
{txt}       Model {c |} {res} 3242.83337        28  115.815478   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 16988.9218     5,388  3.15310353   {txt}R-squared       ={res}    0.1603
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1560
{txt}       Total {c |} {res} 20231.7552     5,416  3.73555302   {txt}Root MSE        =   {res} 1.7756

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            trustel{col 21}{c |} Coefficient{col 33}  Std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}direct {c |}{col 21}{res}{space 2}-.1663037{col 33}{space 2} .0483883{col 44}{space 1}   -3.44{col 53}{space 3}0.001{col 61}{space 4}-.2611643{col 74}{space 3}-.0714431
{txt}{space 14}woman {c |}{col 21}{res}{space 2}-.0384678{col 33}{space 2} .0487228{col 44}{space 1}   -0.79{col 53}{space 3}0.430{col 61}{space 4}-.1339841{col 74}{space 3} .0570486
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}-.1323223{col 33}{space 2} .0722018{col 44}{space 1}   -1.83{col 53}{space 3}0.067{col 61}{space 4}-.2738671{col 74}{space 3} .0092225
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2} .2548006{col 33}{space 2} .0913733{col 44}{space 1}    2.79{col 53}{space 3}0.005{col 61}{space 4} .0756719{col 74}{space 3} .4339293
{txt}{space 16}edr {c |}{col 21}{res}{space 2}-.0001158{col 33}{space 2} .1145255{col 44}{space 1}   -0.00{col 53}{space 3}0.999{col 61}{space 4}-.2246321{col 74}{space 3} .2244004
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .2684728{col 33}{space 2} .0542914{col 44}{space 1}    4.95{col 53}{space 3}0.000{col 61}{space 4} .1620397{col 74}{space 3} .3749059
{txt}{space 17}m1 {c |}{col 21}{res}{space 2} 2.448473{col 33}{space 2} .1024013{col 44}{space 1}   23.91{col 53}{space 3}0.000{col 61}{space 4} 2.247725{col 74}{space 3} 2.649221
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2}-.0130976{col 33}{space 2} .1300226{col 44}{space 1}   -0.10{col 53}{space 3}0.920{col 61}{space 4}-.2679945{col 74}{space 3} .2417994
{txt}{space 7}El Salvador  {c |}{col 21}{res}{space 2} .3530516{col 33}{space 2} .1844603{col 44}{space 1}    1.91{col 53}{space 3}0.056{col 61}{space 4}-.0085651{col 74}{space 3} .7146683
{txt}{space 10}Honduras  {c |}{col 21}{res}{space 2}-.4088434{col 33}{space 2} .1186756{col 44}{space 1}   -3.45{col 53}{space 3}0.001{col 61}{space 4}-.6414956{col 74}{space 3}-.1761912
{txt}{space 9}Nicaragua  {c |}{col 21}{res}{space 2}-.0694708{col 33}{space 2} .1630469{col 44}{space 1}   -0.43{col 53}{space 3}0.670{col 61}{space 4}-.3891086{col 74}{space 3}  .250167
{txt}{space 8}Costa Rica  {c |}{col 21}{res}{space 2} .8402459{col 33}{space 2} .2406835{col 44}{space 1}    3.49{col 53}{space 3}0.000{col 61}{space 4}  .368409{col 74}{space 3} 1.312083
{txt}{space 12}Panama  {c |}{col 21}{res}{space 2} .3738201{col 33}{space 2} .1618286{col 44}{space 1}    2.31{col 53}{space 3}0.021{col 61}{space 4} .0565705{col 74}{space 3} .6910696
{txt}{space 10}Colombia  {c |}{col 21}{res}{space 2}-.3484554{col 33}{space 2} .1470349{col 44}{space 1}   -2.37{col 53}{space 3}0.018{col 61}{space 4}-.6367032{col 74}{space 3}-.0602075
{txt}{space 11}Ecuador  {c |}{col 21}{res}{space 2} .6280705{col 33}{space 2} .1452955{col 44}{space 1}    4.32{col 53}{space 3}0.000{col 61}{space 4} .3432325{col 74}{space 3} .9129085
{txt}{space 11}Bolivia  {c |}{col 21}{res}{space 2} .0873862{col 33}{space 2} .1347928{col 44}{space 1}    0.65{col 53}{space 3}0.517{col 61}{space 4}-.1768623{col 74}{space 3} .3516347
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2} .2635396{col 33}{space 2}  .164731{col 44}{space 1}    1.60{col 53}{space 3}0.110{col 61}{space 4}-.0593998{col 74}{space 3}  .586479
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2}  .218337{col 33}{space 2} .1307953{col 44}{space 1}    1.67{col 53}{space 3}0.095{col 61}{space 4}-.0380746{col 74}{space 3} .4747487
{txt}{space 13}Chile  {c |}{col 21}{res}{space 2} 1.048072{col 33}{space 2} .2784902{col 44}{space 1}    3.76{col 53}{space 3}0.000{col 61}{space 4} .5021186{col 74}{space 3} 1.594025
{txt}{space 11}Uruguay  {c |}{col 21}{res}{space 2}  1.69669{col 33}{space 2} .1990927{col 44}{space 1}    8.52{col 53}{space 3}0.000{col 61}{space 4} 1.306387{col 74}{space 3} 2.086992
{txt}{space 12}Brazil  {c |}{col 21}{res}{space 2}-.5158428{col 33}{space 2} .1327091{col 44}{space 1}   -3.89{col 53}{space 3}0.000{col 61}{space 4}-.7760062{col 74}{space 3}-.2556794
{txt}{space 9}Venezuela  {c |}{col 21}{res}{space 2} .6748691{col 33}{space 2} .2179303{col 44}{space 1}    3.10{col 53}{space 3}0.002{col 61}{space 4} .2476377{col 74}{space 3} 1.102101
{txt}{space 9}Argentina  {c |}{col 21}{res}{space 2} .7893886{col 33}{space 2} .2376303{col 44}{space 1}    3.32{col 53}{space 3}0.001{col 61}{space 4} .3235371{col 74}{space 3}  1.25524
{txt}Dominican Republic  {c |}{col 21}{res}{space 2}-.6092498{col 33}{space 2} .1137252{col 44}{space 1}   -5.36{col 53}{space 3}0.000{col 61}{space 4}-.8321972{col 74}{space 3}-.3863024
{txt}{space 13}Haiti  {c |}{col 21}{res}{space 2}-.9453275{col 33}{space 2} .1464787{col 44}{space 1}   -6.45{col 53}{space 3}0.000{col 61}{space 4}-1.232485{col 74}{space 3}-.6581701
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2}-.3146093{col 33}{space 2} .1614575{col 44}{space 1}   -1.95{col 53}{space 3}0.051{col 61}{space 4}-.6311312{col 74}{space 3} .0019126
{txt}{space 12}Guyana  {c |}{col 21}{res}{space 2}-.0720752{col 33}{space 2}  .245697{col 44}{space 1}   -0.29{col 53}{space 3}0.769{col 61}{space 4}-.5537407{col 74}{space 3} .4095903
{txt}{space 12}Belize  {c |}{col 21}{res}{space 2}  .156691{col 33}{space 2} .1197803{col 44}{space 1}    1.31{col 53}{space 3}0.191{col 61}{space 4}-.0781269{col 74}{space 3} .3915089
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} 2.196782{col 33}{space 2} .1354397{col 44}{space 1}   16.22{col 53}{space 3}0.000{col 61}{space 4} 1.931265{col 74}{space 3} 2.462298
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using table1.doc, dec(2) append ctitle(Direct, matched) drop(i.pais)
{txt}{stata `"shellout using `"table1.doc"'"':table1.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "table1.txt""':seeout}

{com}. 
.         
.                                 
. *** Figure 1
. * Quintiles of approval
. sum approval, detail

                   {txt}Approval of Vote Buying
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}     10,428
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}     10,428

{txt}50%    {res}      .25                      {txt}Mean          {res} .3296653
                        {txt}Largest       Std. dev.     {res} .2778991
{txt}75%    {res}       .5              1
{txt}90%    {res}      .75              1       {txt}Variance      {res} .0772279
{txt}95%    {res}        1              1       {txt}Skewness      {res} .7297272
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 2.929192
{txt}
{com}.         sum approval if !missing(trustel, indirect, approval, woman, quintall, agecohort, edr, rural, m1), detail

                   {txt}Approval of Vote Buying
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      8,277
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      8,277

{txt}50%    {res}      .25                      {txt}Mean          {res}  .330917
                        {txt}Largest       Std. dev.     {res} .2819417
{txt}75%    {res}       .5              1
{txt}90%    {res}      .75              1       {txt}Variance      {res} .0794911
{txt}95%    {res}        1              1       {txt}Skewness      {res} .7174083
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 2.867583
{txt}
{com}.         sum approval if !missing(trustel, direct, approval, woman, quintall, agecohort, edr, rural, m1), detail

                   {txt}Approval of Vote Buying
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      8,353
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      8,353

{txt}50%    {res}      .25                      {txt}Mean          {res} .3310487
                        {txt}Largest       Std. dev.     {res} .2821741
{txt}75%    {res}       .5              1
{txt}90%    {res}      .75              1       {txt}Variance      {res} .0796222
{txt}95%    {res}        1              1       {txt}Skewness      {res} .7155647
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 2.861038
{txt}
{com}.         // 25%=0, 50%=.25, 75%=.5, 95~100%=1
.         recode approval (0=0 "25%")(.25=1 "50%")(.5=2 "75%")(.75 1=3 "100%"), gen(approval_percentiles)
{txt}(7,815 differences between {bf:approval} and {bf:approval_percentiles})

{com}.         lab var approval_percentiles "Approval of Vote Buying, Quintiles"
{txt}
{com}. * figure
. mixed trustel i.indirect##c.approval_percentiles woman quintall agecohort edr rural m1 || pais: 
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log likelihood = {res:-16514.349}  
Iteration 1:{space 2}Log likelihood = {res:-16514.349}  (backed up)
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects ML regression{col 53}Number of obs{col 69} = {res}  8,277
{txt}Group variable: {res}pais{col 53}{txt}Number of groups{col 69} = {res}      6
{txt}{col 53}Obs per group:
{col 66}min = {res}  1,224
{txt}{col 66}avg = {res}1,379.5
{txt}{col 66}max = {res}  1,461
{col 53}{txt}Wald chi2({res}9{txt}){col 69} = {res} 929.27
{txt}Log likelihood = {res}-16514.349{col 53}{txt}Prob > chi2{col 69} = {res} 0.0000

{txt}{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                        trustel{col 33}{c |} Coefficient{col 45}  Std. err.{col 57}      z{col 65}   P>|z|{col 73}     [95% con{col 86}f. interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}1.indirect {c |}{col 33}{res}{space 2}-.1674175{col 45}{space 2}  .069445{col 56}{space 1}   -2.41{col 65}{space 3}0.016{col 73}{space 4}-.3035272{col 86}{space 3}-.0313079
{txt}{space 11}approval_percentiles {c |}{col 33}{res}{space 2} .0533788{col 45}{space 2} .0246063{col 56}{space 1}    2.17{col 65}{space 3}0.030{col 73}{space 4} .0051514{col 86}{space 3} .1016062
{txt}{space 31} {c |}
indirect#c.approval_percentiles {c |}
{space 29}1  {c |}{col 33}{res}{space 2}-.0150878{col 45}{space 2} .0411927{col 56}{space 1}   -0.37{col 65}{space 3}0.714{col 73}{space 4} -.095824{col 86}{space 3} .0656484
{txt}{space 31} {c |}
{space 26}woman {c |}{col 33}{res}{space 2}-.1184393{col 45}{space 2} .0393629{col 56}{space 1}   -3.01{col 65}{space 3}0.003{col 73}{space 4}-.1955891{col 86}{space 3}-.0412896
{txt}{space 23}quintall {c |}{col 33}{res}{space 2} .0248898{col 45}{space 2} .0598494{col 56}{space 1}    0.42{col 65}{space 3}0.678{col 73}{space 4}-.0924129{col 86}{space 3} .1421925
{txt}{space 22}agecohort {c |}{col 33}{res}{space 2} .2788927{col 45}{space 2} .0676383{col 56}{space 1}    4.12{col 65}{space 3}0.000{col 73}{space 4}  .146324{col 86}{space 3} .4114614
{txt}{space 28}edr {c |}{col 33}{res}{space 2}-.1808261{col 45}{space 2} .0942224{col 56}{space 1}   -1.92{col 65}{space 3}0.055{col 73}{space 4}-.3654986{col 86}{space 3} .0038465
{txt}{space 26}rural {c |}{col 33}{res}{space 2} .1614466{col 45}{space 2} .0437741{col 56}{space 1}    3.69{col 65}{space 3}0.000{col 73}{space 4}  .075651{col 86}{space 3} .2472422
{txt}{space 29}m1 {c |}{col 33}{res}{space 2} 2.304325{col 45}{space 2} .0835064{col 56}{space 1}   27.59{col 65}{space 3}0.000{col 73}{space 4} 2.140656{col 86}{space 3} 2.467995
{txt}{space 26}_cons {c |}{col 33}{res}{space 2} 2.210913{col 45}{space 2} .1461457{col 56}{space 1}   15.13{col 65}{space 3}0.000{col 73}{space 4} 1.924473{col 86}{space 3} 2.497354
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}Std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}pais{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33} .0645276{col 44} .0386885{col 58} .0199248{col 70} .2089761
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 3.158501{col 44} .0491154{col 58} 3.063689{col 70} 3.256247
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}135.55{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}
{com}.         margins, dydx(indirect) at(approval_percentiles=(0 1 2 3))
{res}
{txt}{col 1}Average marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:8,277}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.indirect}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:3}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.indirect  {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.indirect   {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.1674175{col 26}{space 2}  .069445{col 37}{space 1}   -2.41{col 46}{space 3}0.016{col 54}{space 4}-.3035272{col 67}{space 3}-.0313079
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.1825053{col 26}{space 2} .0457602{col 37}{space 1}   -3.99{col 46}{space 3}0.000{col 54}{space 4}-.2721936{col 67}{space 3}-.0928171
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1975931{col 26}{space 2} .0525268{col 37}{space 1}   -3.76{col 46}{space 3}0.000{col 54}{space 4}-.3005437{col 67}{space 3}-.0946426
{txt}{space 10}4  {c |}{col 14}{res}{space 2} -.212681{col 26}{space 2}   .08257{col 37}{space 1}   -2.58{col 46}{space 3}0.010{col 54}{space 4}-.3745152{col 67}{space 3}-.0508467
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) ///
>                 ytitle("Effects on Trust in Elections") ///
>                 title("Indirect Exposure to Vote Buying")
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:approval_percentiles}{p_end}
{res}{txt}
{com}.         graph save Graph "fig1_indirect.gph", replace                   
{res}{txt}file {bf:fig1_indirect.gph} saved

{com}. mixed trustel i.direct##c.approval_percentiles woman quintall agecohort edr rural m1 || pais: 
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log likelihood = {res:-16668.992}  
Iteration 1:{space 2}Log likelihood = {res:-16668.992}  (backed up)
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects ML regression{col 53}Number of obs{col 69} = {res}  8,353
{txt}Group variable: {res}pais{col 53}{txt}Number of groups{col 69} = {res}      6
{txt}{col 53}Obs per group:
{col 66}min = {res}  1,258
{txt}{col 66}avg = {res}1,392.2
{txt}{col 66}max = {res}  1,464
{col 53}{txt}Wald chi2({res}9{txt}){col 69} = {res} 941.53
{txt}Log likelihood = {res}-16668.992{col 53}{txt}Prob > chi2{col 69} = {res} 0.0000

{txt}{hline 30}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                      trustel{col 31}{c |} Coefficient{col 43}  Std. err.{col 55}      z{col 63}   P>|z|{col 71}     [95% con{col 84}f. interval]
{hline 30}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}1.direct {c |}{col 31}{res}{space 2}-.2150162{col 43}{space 2} .0877818{col 54}{space 1}   -2.45{col 63}{space 3}0.014{col 71}{space 4}-.3870653{col 84}{space 3} -.042967
{txt}{space 9}approval_percentiles {c |}{col 31}{res}{space 2} .0584097{col 43}{space 2} .0220585{col 54}{space 1}    2.65{col 63}{space 3}0.008{col 71}{space 4} .0151758{col 84}{space 3} .1016436
{txt}{space 29} {c |}
direct#c.approval_percentiles {c |}
{space 27}1  {c |}{col 31}{res}{space 2}-.0338213{col 43}{space 2} .0501716{col 54}{space 1}   -0.67{col 63}{space 3}0.500{col 71}{space 4}-.1321558{col 84}{space 3} .0645132
{txt}{space 29} {c |}
{space 24}woman {c |}{col 31}{res}{space 2}-.1208291{col 43}{space 2} .0392089{col 54}{space 1}   -3.08{col 63}{space 3}0.002{col 71}{space 4}-.1976771{col 84}{space 3}-.0439812
{txt}{space 21}quintall {c |}{col 31}{res}{space 2} .0115417{col 43}{space 2} .0595535{col 54}{space 1}    0.19{col 63}{space 3}0.846{col 71}{space 4} -.105181{col 84}{space 3} .1282645
{txt}{space 20}agecohort {c |}{col 31}{res}{space 2} .2789406{col 43}{space 2}  .066956{col 54}{space 1}    4.17{col 63}{space 3}0.000{col 71}{space 4} .1477094{col 84}{space 3} .4101719
{txt}{space 26}edr {c |}{col 31}{res}{space 2}-.1884521{col 43}{space 2} .0937934{col 54}{space 1}   -2.01{col 63}{space 3}0.045{col 71}{space 4}-.3722838{col 84}{space 3}-.0046204
{txt}{space 24}rural {c |}{col 31}{res}{space 2} .1595226{col 43}{space 2} .0436007{col 54}{space 1}    3.66{col 63}{space 3}0.000{col 71}{space 4} .0740668{col 84}{space 3} .2449783
{txt}{space 27}m1 {c |}{col 31}{res}{space 2} 2.306374{col 43}{space 2} .0831958{col 54}{space 1}   27.72{col 63}{space 3}0.000{col 71}{space 4} 2.143313{col 84}{space 3} 2.469435
{txt}{space 24}_cons {c |}{col 31}{res}{space 2} 2.200306{col 43}{space 2} .1458849{col 54}{space 1}   15.08{col 63}{space 3}0.000{col 71}{space 4} 1.914377{col 84}{space 3} 2.486236
{txt}{hline 30}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}Std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}pais{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33} .0663483{col 44} .0397189{col 58} .0205241{col 70} .2144842
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 3.160765{col 44} .0489263{col 58} 3.066311{col 70} 3.258129
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}141.86{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}
{com}.         margins, dydx(direct) at(approval_percentiles=(0 1 2 3))
{res}
{txt}{col 1}Average marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:8,353}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.direct}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:3}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.direct    {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.direct     {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.2150162{col 26}{space 2} .0877818{col 37}{space 1}   -2.45{col 46}{space 3}0.014{col 54}{space 4}-.3870653{col 67}{space 3} -.042967
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.2488375{col 26}{space 2} .0581152{col 37}{space 1}   -4.28{col 46}{space 3}0.000{col 54}{space 4}-.3627412{col 67}{space 3}-.1349337
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.2826588{col 26}{space 2} .0639022{col 37}{space 1}   -4.42{col 46}{space 3}0.000{col 54}{space 4}-.4079048{col 67}{space 3}-.1574128
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.3164801{col 26}{space 2}  .099116{col 37}{space 1}   -3.19{col 46}{space 3}0.001{col 54}{space 4}-.5107439{col 67}{space 3}-.1222163
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) ///
>                 ytitle("Effects on Trust in Elections") ///
>                 title("Direct Exposure to Vote Buying")
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:approval_percentiles}{p_end}
{res}{txt}
{com}.         graph save Graph "fig1_direct.gph", replace             
{res}{txt}file {bf:fig1_direct.gph} saved

{com}. graph combine fig1_direct.gph fig1_indirect.gph, xsize(8) ysize(4) title("")
{res}{txt}
{com}. graph save Graph "fig1.gph", replace
{res}{txt}file {bf:fig1.gph} saved

{com}. 
. * figure note interquartile difference analysis
. mixed trustel i.indirect##c.approval woman quintall agecohort edr rural m1 || pais: 
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log likelihood = {res: -16515.32}  
Iteration 1:{space 2}Log likelihood = {res: -16515.32}  (backed up)
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects ML regression{col 53}Number of obs{col 69} = {res}  8,277
{txt}Group variable: {res}pais{col 53}{txt}Number of groups{col 69} = {res}      6
{txt}{col 53}Obs per group:
{col 66}min = {res}  1,224
{txt}{col 66}avg = {res}1,379.5
{txt}{col 66}max = {res}  1,461
{col 53}{txt}Wald chi2({res}9{txt}){col 69} = {res} 927.11
{txt}Log likelihood = {res} -16515.32{col 53}{txt}Prob > chi2{col 69} = {res} 0.0000

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            trustel{col 21}{c |} Coefficient{col 33}  Std. err.{col 45}      z{col 53}   P>|z|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}1.indirect {c |}{col 21}{res}{space 2}-.1594206{col 33}{space 2} .0665388{col 44}{space 1}   -2.40{col 53}{space 3}0.017{col 61}{space 4}-.2898342{col 74}{space 3} -.029007
{txt}{space 11}approval {c |}{col 21}{res}{space 2} .1660684{col 33}{space 2}  .089227{col 44}{space 1}    1.86{col 53}{space 3}0.063{col 61}{space 4}-.0088133{col 74}{space 3} .3409501
{txt}{space 19} {c |}
indirect#c.approval {c |}
{space 17}1  {c |}{col 21}{res}{space 2}-.0818036{col 33}{space 2} .1443307{col 44}{space 1}   -0.57{col 53}{space 3}0.571{col 61}{space 4}-.3646865{col 74}{space 3} .2010794
{txt}{space 19} {c |}
{space 14}woman {c |}{col 21}{res}{space 2}-.1181147{col 33}{space 2} .0393905{col 44}{space 1}   -3.00{col 53}{space 3}0.003{col 61}{space 4}-.1953188{col 74}{space 3}-.0409107
{txt}{space 11}quintall {c |}{col 21}{res}{space 2} .0231046{col 33}{space 2} .0598481{col 44}{space 1}    0.39{col 53}{space 3}0.699{col 61}{space 4}-.0941955{col 74}{space 3} .1404046
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2} .2750234{col 33}{space 2} .0676354{col 44}{space 1}    4.07{col 53}{space 3}0.000{col 61}{space 4} .1424604{col 74}{space 3} .4075864
{txt}{space 16}edr {c |}{col 21}{res}{space 2}-.1872798{col 33}{space 2} .0941716{col 44}{space 1}   -1.99{col 53}{space 3}0.047{col 61}{space 4}-.3718528{col 74}{space 3}-.0027069
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .1621326{col 33}{space 2} .0437819{col 44}{space 1}    3.70{col 53}{space 3}0.000{col 61}{space 4} .0763218{col 74}{space 3} .2479435
{txt}{space 17}m1 {c |}{col 21}{res}{space 2} 2.306525{col 33}{space 2} .0835066{col 44}{space 1}   27.62{col 53}{space 3}0.000{col 61}{space 4} 2.142855{col 74}{space 3} 2.470195
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} 2.228617{col 33}{space 2} .1454794{col 44}{space 1}   15.32{col 53}{space 3}0.000{col 61}{space 4} 1.943483{col 74}{space 3} 2.513751
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}Std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}pais{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33} .0642443{col 44} .0385305{col 58} .0198301{col 70} .2081338
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 3.159252{col 44} .0491271{col 58} 3.064417{col 70} 3.257022
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}134.31{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}
{com}.         margins, dydx(indirect) at(approval=(0 .5)) 
{res}
{txt}{col 1}Average marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:8,277}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.indirect}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 8:approval} = {res:{ralign 2:0}}
{lalign 7:2._at: }{space 0}{lalign 8:approval} = {res:{ralign 2:.5}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.indirect  {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.indirect   {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.1594206{col 26}{space 2} .0665388{col 37}{space 1}   -2.40{col 46}{space 3}0.017{col 54}{space 4}-.2898342{col 67}{space 3} -.029007
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.2003224{col 26}{space 2} .0493703{col 37}{space 1}   -4.06{col 46}{space 3}0.000{col 54}{space 4}-.2970865{col 67}{space 3}-.1035583
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         // diff= -.041, standard error=.083, z-statistic=-.494, one-sided p-value=.311
. mixed trustel i.direct##c.approval woman quintall agecohort edr rural m1 || pais: 
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log likelihood = {res:-16670.085}  
Iteration 1:{space 2}Log likelihood = {res:-16670.085}  (backed up)
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects ML regression{col 53}Number of obs{col 69} = {res}  8,353
{txt}Group variable: {res}pais{col 53}{txt}Number of groups{col 69} = {res}      6
{txt}{col 53}Obs per group:
{col 66}min = {res}  1,258
{txt}{col 66}avg = {res}1,392.2
{txt}{col 66}max = {res}  1,464
{col 53}{txt}Wald chi2({res}9{txt}){col 69} = {res} 939.09
{txt}Log likelihood = {res}-16670.085{col 53}{txt}Prob > chi2{col 69} = {res} 0.0000

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          trustel{col 19}{c |} Coefficient{col 31}  Std. err.{col 43}      z{col 51}   P>|z|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}1.direct {c |}{col 19}{res}{space 2}-.2087534{col 31}{space 2} .0838486{col 42}{space 1}   -2.49{col 51}{space 3}0.013{col 59}{space 4}-.3730936{col 72}{space 3}-.0444132
{txt}{space 9}approval {c |}{col 19}{res}{space 2} .1776808{col 31}{space 2} .0792929{col 42}{space 1}    2.24{col 51}{space 3}0.025{col 59}{space 4} .0222695{col 72}{space 3} .3330921
{txt}{space 17} {c |}
direct#c.approval {c |}
{space 15}1  {c |}{col 19}{res}{space 2}-.1442635{col 31}{space 2} .1730676{col 42}{space 1}   -0.83{col 51}{space 3}0.405{col 59}{space 4}-.4834698{col 72}{space 3} .1949428
{txt}{space 17} {c |}
{space 12}woman {c |}{col 19}{res}{space 2}-.1204277{col 31}{space 2} .0392372{col 42}{space 1}   -3.07{col 51}{space 3}0.002{col 59}{space 4}-.1973311{col 72}{space 3}-.0435243
{txt}{space 9}quintall {c |}{col 19}{res}{space 2} .0096415{col 31}{space 2} .0595505{col 42}{space 1}    0.16{col 51}{space 3}0.871{col 59}{space 4}-.1070754{col 72}{space 3} .1263584
{txt}{space 8}agecohort {c |}{col 19}{res}{space 2} .2748912{col 31}{space 2} .0669641{col 42}{space 1}    4.11{col 51}{space 3}0.000{col 59}{space 4}  .143644{col 72}{space 3} .4061384
{txt}{space 14}edr {c |}{col 19}{res}{space 2} -.195217{col 31}{space 2} .0937456{col 42}{space 1}   -2.08{col 51}{space 3}0.037{col 59}{space 4}-.3789549{col 72}{space 3} -.011479
{txt}{space 12}rural {c |}{col 19}{res}{space 2} .1601706{col 31}{space 2} .0436113{col 42}{space 1}    3.67{col 51}{space 3}0.000{col 59}{space 4}  .074694{col 72}{space 3} .2456473
{txt}{space 15}m1 {c |}{col 19}{res}{space 2} 2.308562{col 31}{space 2} .0831997{col 42}{space 1}   27.75{col 51}{space 3}0.000{col 59}{space 4} 2.145493{col 72}{space 3}  2.47163
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} 2.220614{col 31}{space 2} .1453214{col 42}{space 1}   15.28{col 51}{space 3}0.000{col 59}{space 4} 1.935789{col 72}{space 3} 2.505439
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}Std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}pais{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33} .0661368{col 44} .0396016{col 58}  .020453{col 70} .2138595
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 3.161601{col 44} .0489393{col 58} 3.067122{col 70}  3.25899
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}140.82{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}
{com}.         margins, dydx(direct) at(approval=(0 .5))
{res}
{txt}{col 1}Average marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:8,353}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.direct}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 8:approval} = {res:{ralign 2:0}}
{lalign 7:2._at: }{space 0}{lalign 8:approval} = {res:{ralign 2:.5}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.direct    {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.direct     {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.2087534{col 26}{space 2} .0838486{col 37}{space 1}   -2.49{col 46}{space 3}0.013{col 54}{space 4}-.3730936{col 67}{space 3}-.0444132
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.2808851{col 26}{space 2} .0600632{col 37}{space 1}   -4.68{col 46}{space 3}0.000{col 54}{space 4}-.3986068{col 67}{space 3}-.1631635
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         // diff=-.072, se=.103, z=.699, one-sided p-value=.242
. 
. 
. *** Experiment analyses for Table 2 and Figure 2
. import delimited "../data/mex_experiment_cps.csv", clear
{res}{txt}(encoding automatically selected: UTF-8)
{text}(20 vars, 1,779 obs)

{com}. destring, replace
{txt}durationinseconds already numeric; no {res}replace
{txt}responseid: contains nonnumeric characters; no {res}replace
{txt}q2 already numeric; no {res}replace
{txt}birth already numeric; no {res}replace
{txt}citizenship already numeric; no {res}replace
{txt}gender already numeric; no {res}replace
{txt}b47b already numeric; no {res}replace
{txt}norms1 already numeric; no {res}replace
{txt}norms2 already numeric; no {res}replace
{txt}q78_firstclick already numeric; no {res}replace
{txt}exp1a already numeric; no {res}replace
{txt}exp1b already numeric; no {res}replace
{txt}exp1c already numeric; no {res}replace
{txt}b47a already numeric; no {res}replace
{txt}b13 already numeric; no {res}replace
{txt}b20 already numeric; no {res}replace
{txt}b81 already numeric; no {res}replace
{txt}ed already numeric; no {res}replace
{txt}etid already numeric; no {res}replace
{txt}religid already numeric; no {res}replace
{txt}
{com}. 
. *dropping invalid responses
. note q2: "Consent"
{res}{txt}
{com}.         drop if q2!=1 
{txt}(33 observations deleted)

{com}. note citizenship: "Mexican citizen"
{res}{txt}
{com}.         drop if citizenship!=1 
{txt}(35 observations deleted)

{com}. note birth: "How old are you? [in years]"
{res}{txt}
{com}.         drop if birth==1
{txt}(2 observations deleted)

{com}. 
. *creating treatment variable using a timer item that was only measured for treated
. gen treat=.
{txt}(1,709 missing values generated)

{com}. replace treat=1 if q78_firstclick!=.
{txt}(791 real changes made)

{com}. replace treat=0 if q78_firstclick==.
{txt}(918 real changes made)

{com}. 
. *norms
. recode norms1 (1=5)(2=4)(4=2)(5=1)
{txt}(1,262 changes made to {bf:norms1})

{com}.         recode norms2 (1=5)(2=4)(4=2)(5=1)
{txt}(1,137 changes made to {bf:norms2})

{com}.         lab define approve1 5"Strongly approve" 4"Approve" 3"Do not approve but understand" 2"Disapprove" 1"Strongly disapprove"
{txt}
{com}.         lab val norms1 approve1
{txt}
{com}.         lab val norms2 approve1
{txt}
{com}.         lab var norms1 "Approve parties buying votes"
{txt}
{com}.         lab var norms2 "Approve voters selling votes"
{txt}
{com}. gen norm=(norms1+norms2)/2
{txt}(47 missing values generated)

{com}. rescale norm 0 1
{txt}(1,662 real changes made)

{com}. 
. *trust in elections
. tab b47a

       {txt}B47A {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}        392       24.65       24.65
{txt}          3 {c |}{res}        245       15.41       40.06
{txt}          4 {c |}{res}        336       21.13       61.19
{txt}          5 {c |}{res}        271       17.04       78.24
{txt}          6 {c |}{res}         97        6.10       84.34
{txt}          7 {c |}{res}         53        3.33       87.67
{txt}          8 {c |}{res}        196       12.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,590      100.00
{txt}
{com}. gen trustel=b47a-1
{txt}(119 missing values generated)

{com}. 
. *saturation
. note exp1c: "Have you seen news stories like this before?"
{res}{txt}
{com}. gen unsaturated=1
{txt}
{com}. replace unsaturated=0 if exp1c==1
{txt}(336 real changes made)

{com}. recode exp1c(2=0)
{txt}(454 changes made to {bf:exp1c})

{com}. 
. *creating variable excluding those who took less than .3 seconds per word for shortest way through survey (faster than approximately 427 seconds)
. tab durationinseconds if etid!=. | religid!=. // fastest 2.43% categorized as speeders using this cutoff

   {txt}Duration {c |}
        (in {c |}
   seconds) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        212 {c |}{res}          1        0.07        0.07
{txt}        214 {c |}{res}          1        0.07        0.13
{txt}        238 {c |}{res}          1        0.07        0.20
{txt}        250 {c |}{res}          1        0.07        0.26
{txt}        255 {c |}{res}          1        0.07        0.33
{txt}        256 {c |}{res}          1        0.07        0.39
{txt}        267 {c |}{res}          2        0.13        0.52
{txt}        276 {c |}{res}          1        0.07        0.59
{txt}        283 {c |}{res}          1        0.07        0.66
{txt}        284 {c |}{res}          1        0.07        0.72
{txt}        294 {c |}{res}          1        0.07        0.79
{txt}        297 {c |}{res}          1        0.07        0.85
{txt}        306 {c |}{res}          1        0.07        0.92
{txt}        314 {c |}{res}          1        0.07        0.98
{txt}        317 {c |}{res}          1        0.07        1.05
{txt}        335 {c |}{res}          1        0.07        1.11
{txt}        336 {c |}{res}          2        0.13        1.25
{txt}        355 {c |}{res}          1        0.07        1.31
{txt}        366 {c |}{res}          1        0.07        1.38
{txt}        368 {c |}{res}          1        0.07        1.44
{txt}        376 {c |}{res}          1        0.07        1.51
{txt}        384 {c |}{res}          1        0.07        1.57
{txt}        385 {c |}{res}          1        0.07        1.64
{txt}        388 {c |}{res}          1        0.07        1.70
{txt}        393 {c |}{res}          2        0.13        1.84
{txt}        396 {c |}{res}          1        0.07        1.90
{txt}        397 {c |}{res}          1        0.07        1.97
{txt}        400 {c |}{res}          1        0.07        2.03
{txt}        406 {c |}{res}          1        0.07        2.10
{txt}        411 {c |}{res}          1        0.07        2.16
{txt}        419 {c |}{res}          2        0.13        2.30
{txt}        423 {c |}{res}          1        0.07        2.36
{txt}        427 {c |}{res}          1        0.07        2.43
{txt}        428 {c |}{res}          1        0.07        2.49
{txt}        435 {c |}{res}          1        0.07        2.56
{txt}        439 {c |}{res}          1        0.07        2.62
{txt}        440 {c |}{res}          1        0.07        2.69
{txt}        442 {c |}{res}          1        0.07        2.75
{txt}        446 {c |}{res}          1        0.07        2.82
{txt}        448 {c |}{res}          2        0.13        2.95
{txt}        451 {c |}{res}          1        0.07        3.02
{txt}        453 {c |}{res}          1        0.07        3.08
{txt}        454 {c |}{res}          1        0.07        3.15
{txt}        455 {c |}{res}          1        0.07        3.21
{txt}        456 {c |}{res}          2        0.13        3.34
{txt}        457 {c |}{res}          1        0.07        3.41
{txt}        458 {c |}{res}          1        0.07        3.48
{txt}        459 {c |}{res}          1        0.07        3.54
{txt}        462 {c |}{res}          1        0.07        3.61
{txt}        463 {c |}{res}          1        0.07        3.67
{txt}        465 {c |}{res}          1        0.07        3.74
{txt}        468 {c |}{res}          2        0.13        3.87
{txt}        469 {c |}{res}          1        0.07        3.93
{txt}        472 {c |}{res}          1        0.07        4.00
{txt}        476 {c |}{res}          1        0.07        4.07
{txt}        477 {c |}{res}          1        0.07        4.13
{txt}        479 {c |}{res}          3        0.20        4.33
{txt}        483 {c |}{res}          1        0.07        4.39
{txt}        484 {c |}{res}          1        0.07        4.46
{txt}        485 {c |}{res}          1        0.07        4.52
{txt}        488 {c |}{res}          2        0.13        4.66
{txt}        489 {c |}{res}          1        0.07        4.72
{txt}        490 {c |}{res}          1        0.07        4.79
{txt}        492 {c |}{res}          1        0.07        4.85
{txt}        494 {c |}{res}          2        0.13        4.98
{txt}        495 {c |}{res}          2        0.13        5.11
{txt}        498 {c |}{res}          2        0.13        5.25
{txt}        500 {c |}{res}          2        0.13        5.38
{txt}        505 {c |}{res}          1        0.07        5.44
{txt}        508 {c |}{res}          2        0.13        5.57
{txt}        510 {c |}{res}          1        0.07        5.64
{txt}        512 {c |}{res}          1        0.07        5.70
{txt}        514 {c |}{res}          1        0.07        5.77
{txt}        518 {c |}{res}          1        0.07        5.84
{txt}        522 {c |}{res}          1        0.07        5.90
{txt}        526 {c |}{res}          1        0.07        5.97
{txt}        527 {c |}{res}          2        0.13        6.10
{txt}        528 {c |}{res}          4        0.26        6.36
{txt}        529 {c |}{res}          1        0.07        6.43
{txt}        532 {c |}{res}          1        0.07        6.49
{txt}        534 {c |}{res}          2        0.13        6.62
{txt}        535 {c |}{res}          2        0.13        6.75
{txt}        536 {c |}{res}          1        0.07        6.82
{txt}        537 {c |}{res}          2        0.13        6.95
{txt}        538 {c |}{res}          1        0.07        7.02
{txt}        539 {c |}{res}          1        0.07        7.08
{txt}        540 {c |}{res}          1        0.07        7.15
{txt}        542 {c |}{res}          2        0.13        7.28
{txt}        543 {c |}{res}          3        0.20        7.48
{txt}        544 {c |}{res}          1        0.07        7.54
{txt}        546 {c |}{res}          1        0.07        7.61
{txt}        547 {c |}{res}          1        0.07        7.67
{txt}        548 {c |}{res}          2        0.13        7.80
{txt}        549 {c |}{res}          1        0.07        7.87
{txt}        550 {c |}{res}          1        0.07        7.93
{txt}        556 {c |}{res}          1        0.07        8.00
{txt}        557 {c |}{res}          1        0.07        8.07
{txt}        560 {c |}{res}          2        0.13        8.20
{txt}        564 {c |}{res}          1        0.07        8.26
{txt}        566 {c |}{res}          2        0.13        8.39
{txt}        567 {c |}{res}          2        0.13        8.52
{txt}        569 {c |}{res}          1        0.07        8.59
{txt}        570 {c |}{res}          2        0.13        8.72
{txt}        571 {c |}{res}          3        0.20        8.92
{txt}        572 {c |}{res}          2        0.13        9.05
{txt}        574 {c |}{res}          3        0.20        9.25
{txt}        577 {c |}{res}          3        0.20        9.44
{txt}        578 {c |}{res}          1        0.07        9.51
{txt}        579 {c |}{res}          2        0.13        9.64
{txt}        582 {c |}{res}          2        0.13        9.77
{txt}        583 {c |}{res}          1        0.07        9.84
{txt}        584 {c |}{res}          1        0.07        9.90
{txt}        585 {c |}{res}          2        0.13       10.03
{txt}        586 {c |}{res}          1        0.07       10.10
{txt}        587 {c |}{res}          1        0.07       10.16
{txt}        589 {c |}{res}          1        0.07       10.23
{txt}        590 {c |}{res}          1        0.07       10.30
{txt}        591 {c |}{res}          1        0.07       10.36
{txt}        592 {c |}{res}          3        0.20       10.56
{txt}        596 {c |}{res}          1        0.07       10.62
{txt}        597 {c |}{res}          1        0.07       10.69
{txt}        598 {c |}{res}          1        0.07       10.75
{txt}        599 {c |}{res}          2        0.13       10.89
{txt}        600 {c |}{res}          1        0.07       10.95
{txt}        602 {c |}{res}          2        0.13       11.08
{txt}        605 {c |}{res}          2        0.13       11.21
{txt}        606 {c |}{res}          1        0.07       11.28
{txt}        607 {c |}{res}          1        0.07       11.34
{txt}        608 {c |}{res}          2        0.13       11.48
{txt}        609 {c |}{res}          2        0.13       11.61
{txt}        610 {c |}{res}          2        0.13       11.74
{txt}        613 {c |}{res}          1        0.07       11.80
{txt}        614 {c |}{res}          3        0.20       12.00
{txt}        615 {c |}{res}          1        0.07       12.07
{txt}        616 {c |}{res}          1        0.07       12.13
{txt}        617 {c |}{res}          1        0.07       12.20
{txt}        619 {c |}{res}          3        0.20       12.39
{txt}        622 {c |}{res}          1        0.07       12.46
{txt}        624 {c |}{res}          2        0.13       12.59
{txt}        625 {c |}{res}          1        0.07       12.66
{txt}        626 {c |}{res}          1        0.07       12.72
{txt}        627 {c |}{res}          1        0.07       12.79
{txt}        628 {c |}{res}          1        0.07       12.85
{txt}        630 {c |}{res}          1        0.07       12.92
{txt}        631 {c |}{res}          1        0.07       12.98
{txt}        632 {c |}{res}          1        0.07       13.05
{txt}        633 {c |}{res}          2        0.13       13.18
{txt}        634 {c |}{res}          3        0.20       13.38
{txt}        635 {c |}{res}          3        0.20       13.57
{txt}        638 {c |}{res}          1        0.07       13.64
{txt}        639 {c |}{res}          1        0.07       13.70
{txt}        641 {c |}{res}          2        0.13       13.84
{txt}        642 {c |}{res}          2        0.13       13.97
{txt}        643 {c |}{res}          1        0.07       14.03
{txt}        644 {c |}{res}          2        0.13       14.16
{txt}        645 {c |}{res}          2        0.13       14.30
{txt}        646 {c |}{res}          1        0.07       14.36
{txt}        647 {c |}{res}          3        0.20       14.56
{txt}        648 {c |}{res}          2        0.13       14.69
{txt}        649 {c |}{res}          2        0.13       14.82
{txt}        652 {c |}{res}          1        0.07       14.89
{txt}        654 {c |}{res}          1        0.07       14.95
{txt}        655 {c |}{res}          3        0.20       15.15
{txt}        656 {c |}{res}          3        0.20       15.34
{txt}        657 {c |}{res}          1        0.07       15.41
{txt}        658 {c |}{res}          2        0.13       15.54
{txt}        659 {c |}{res}          1        0.07       15.61
{txt}        660 {c |}{res}          2        0.13       15.74
{txt}        661 {c |}{res}          2        0.13       15.87
{txt}        662 {c |}{res}          1        0.07       15.93
{txt}        665 {c |}{res}          2        0.13       16.07
{txt}        667 {c |}{res}          1        0.07       16.13
{txt}        668 {c |}{res}          1        0.07       16.20
{txt}        669 {c |}{res}          1        0.07       16.26
{txt}        670 {c |}{res}          3        0.20       16.46
{txt}        671 {c |}{res}          1        0.07       16.52
{txt}        672 {c |}{res}          2        0.13       16.66
{txt}        673 {c |}{res}          2        0.13       16.79
{txt}        674 {c |}{res}          1        0.07       16.85
{txt}        675 {c |}{res}          1        0.07       16.92
{txt}        677 {c |}{res}          2        0.13       17.05
{txt}        678 {c |}{res}          2        0.13       17.18
{txt}        679 {c |}{res}          1        0.07       17.25
{txt}        680 {c |}{res}          2        0.13       17.38
{txt}        681 {c |}{res}          1        0.07       17.44
{txt}        682 {c |}{res}          1        0.07       17.51
{txt}        683 {c |}{res}          1        0.07       17.57
{txt}        684 {c |}{res}          1        0.07       17.64
{txt}        685 {c |}{res}          2        0.13       17.77
{txt}        687 {c |}{res}          1        0.07       17.84
{txt}        688 {c |}{res}          1        0.07       17.90
{txt}        689 {c |}{res}          1        0.07       17.97
{txt}        692 {c |}{res}          2        0.13       18.10
{txt}        693 {c |}{res}          2        0.13       18.23
{txt}        694 {c |}{res}          2        0.13       18.36
{txt}        695 {c |}{res}          3        0.20       18.56
{txt}        698 {c |}{res}          4        0.26       18.82
{txt}        699 {c |}{res}          3        0.20       19.02
{txt}        700 {c |}{res}          1        0.07       19.08
{txt}        701 {c |}{res}          2        0.13       19.21
{txt}        704 {c |}{res}          2        0.13       19.34
{txt}        705 {c |}{res}          2        0.13       19.48
{txt}        706 {c |}{res}          2        0.13       19.61
{txt}        708 {c |}{res}          1        0.07       19.67
{txt}        709 {c |}{res}          2        0.13       19.80
{txt}        710 {c |}{res}          1        0.07       19.87
{txt}        713 {c |}{res}          1        0.07       19.93
{txt}        714 {c |}{res}          1        0.07       20.00
{txt}        715 {c |}{res}          1        0.07       20.07
{txt}        716 {c |}{res}          2        0.13       20.20
{txt}        717 {c |}{res}          2        0.13       20.33
{txt}        718 {c |}{res}          2        0.13       20.46
{txt}        721 {c |}{res}          2        0.13       20.59
{txt}        723 {c |}{res}          2        0.13       20.72
{txt}        724 {c |}{res}          1        0.07       20.79
{txt}        725 {c |}{res}          1        0.07       20.85
{txt}        727 {c |}{res}          2        0.13       20.98
{txt}        728 {c |}{res}          3        0.20       21.18
{txt}        729 {c |}{res}          2        0.13       21.31
{txt}        730 {c |}{res}          2        0.13       21.44
{txt}        731 {c |}{res}          4        0.26       21.70
{txt}        732 {c |}{res}          2        0.13       21.84
{txt}        734 {c |}{res}          2        0.13       21.97
{txt}        735 {c |}{res}          1        0.07       22.03
{txt}        736 {c |}{res}          1        0.07       22.10
{txt}        737 {c |}{res}          1        0.07       22.16
{txt}        738 {c |}{res}          1        0.07       22.23
{txt}        739 {c |}{res}          2        0.13       22.36
{txt}        740 {c |}{res}          2        0.13       22.49
{txt}        741 {c |}{res}          2        0.13       22.62
{txt}        743 {c |}{res}          2        0.13       22.75
{txt}        744 {c |}{res}          1        0.07       22.82
{txt}        745 {c |}{res}          3        0.20       23.02
{txt}        746 {c |}{res}          1        0.07       23.08
{txt}        747 {c |}{res}          1        0.07       23.15
{txt}        748 {c |}{res}          2        0.13       23.28
{txt}        751 {c |}{res}          1        0.07       23.34
{txt}        752 {c |}{res}          1        0.07       23.41
{txt}        754 {c |}{res}          3        0.20       23.61
{txt}        755 {c |}{res}          3        0.20       23.80
{txt}        756 {c |}{res}          1        0.07       23.87
{txt}        758 {c |}{res}          3        0.20       24.07
{txt}        759 {c |}{res}          3        0.20       24.26
{txt}        760 {c |}{res}          2        0.13       24.39
{txt}        762 {c |}{res}          2        0.13       24.52
{txt}        763 {c |}{res}          2        0.13       24.66
{txt}        764 {c |}{res}          4        0.26       24.92
{txt}        765 {c |}{res}          2        0.13       25.05
{txt}        766 {c |}{res}          3        0.20       25.25
{txt}        767 {c |}{res}          2        0.13       25.38
{txt}        769 {c |}{res}          3        0.20       25.57
{txt}        770 {c |}{res}          1        0.07       25.64
{txt}        773 {c |}{res}          1        0.07       25.70
{txt}        774 {c |}{res}          6        0.39       26.10
{txt}        777 {c |}{res}          1        0.07       26.16
{txt}        778 {c |}{res}          1        0.07       26.23
{txt}        779 {c |}{res}          3        0.20       26.43
{txt}        780 {c |}{res}          2        0.13       26.56
{txt}        781 {c |}{res}          1        0.07       26.62
{txt}        782 {c |}{res}          3        0.20       26.82
{txt}        783 {c |}{res}          4        0.26       27.08
{txt}        784 {c |}{res}          2        0.13       27.21
{txt}        786 {c |}{res}          3        0.20       27.41
{txt}        787 {c |}{res}          1        0.07       27.48
{txt}        788 {c |}{res}          3        0.20       27.67
{txt}        790 {c |}{res}          1        0.07       27.74
{txt}        791 {c |}{res}          2        0.13       27.87
{txt}        792 {c |}{res}          2        0.13       28.00
{txt}        793 {c |}{res}          4        0.26       28.26
{txt}        794 {c |}{res}          1        0.07       28.33
{txt}        795 {c |}{res}          3        0.20       28.52
{txt}        796 {c |}{res}          2        0.13       28.66
{txt}        797 {c |}{res}          4        0.26       28.92
{txt}        798 {c |}{res}          3        0.20       29.11
{txt}        799 {c |}{res}          3        0.20       29.31
{txt}        801 {c |}{res}          2        0.13       29.44
{txt}        803 {c |}{res}          3        0.20       29.64
{txt}        804 {c |}{res}          1        0.07       29.70
{txt}        805 {c |}{res}          3        0.20       29.90
{txt}        806 {c |}{res}          2        0.13       30.03
{txt}        807 {c |}{res}          2        0.13       30.16
{txt}        808 {c |}{res}          3        0.20       30.36
{txt}        810 {c |}{res}          3        0.20       30.56
{txt}        811 {c |}{res}          2        0.13       30.69
{txt}        813 {c |}{res}          1        0.07       30.75
{txt}        814 {c |}{res}          1        0.07       30.82
{txt}        815 {c |}{res}          2        0.13       30.95
{txt}        817 {c |}{res}          2        0.13       31.08
{txt}        819 {c |}{res}          1        0.07       31.15
{txt}        820 {c |}{res}          1        0.07       31.21
{txt}        822 {c |}{res}          3        0.20       31.41
{txt}        823 {c |}{res}          3        0.20       31.61
{txt}        826 {c |}{res}          1        0.07       31.67
{txt}        828 {c |}{res}          1        0.07       31.74
{txt}        829 {c |}{res}          1        0.07       31.80
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{txt}       5538 {c |}{res}          1        0.07       95.34
{txt}       5552 {c |}{res}          1        0.07       95.41
{txt}       5557 {c |}{res}          1        0.07       95.48
{txt}       5899 {c |}{res}          1        0.07       95.54
{txt}       5998 {c |}{res}          1        0.07       95.61
{txt}       6062 {c |}{res}          1        0.07       95.67
{txt}       6165 {c |}{res}          1        0.07       95.74
{txt}       6208 {c |}{res}          1        0.07       95.80
{txt}       6349 {c |}{res}          1        0.07       95.87
{txt}       6637 {c |}{res}          1        0.07       95.93
{txt}       7000 {c |}{res}          1        0.07       96.00
{txt}       7200 {c |}{res}          1        0.07       96.07
{txt}       7229 {c |}{res}          1        0.07       96.13
{txt}       7268 {c |}{res}          1        0.07       96.20
{txt}       7404 {c |}{res}          1        0.07       96.26
{txt}       7444 {c |}{res}          1        0.07       96.33
{txt}       7490 {c |}{res}          1        0.07       96.39
{txt}       7829 {c |}{res}          1        0.07       96.46
{txt}       7876 {c |}{res}          1        0.07       96.52
{txt}       7931 {c |}{res}          1        0.07       96.59
{txt}       8067 {c |}{res}          1        0.07       96.66
{txt}       8155 {c |}{res}          1        0.07       96.72
{txt}       8499 {c |}{res}          1        0.07       96.79
{txt}       8689 {c |}{res}          1        0.07       96.85
{txt}       8744 {c |}{res}          1        0.07       96.92
{txt}       9736 {c |}{res}          1        0.07       96.98
{txt}       9819 {c |}{res}          1        0.07       97.05
{txt}       9957 {c |}{res}          1        0.07       97.11
{txt}      11357 {c |}{res}          1        0.07       97.18
{txt}      11369 {c |}{res}          1        0.07       97.25
{txt}      11455 {c |}{res}          1        0.07       97.31
{txt}      11674 {c |}{res}          1        0.07       97.38
{txt}      11994 {c |}{res}          1        0.07       97.44
{txt}      12755 {c |}{res}          1        0.07       97.51
{txt}      13962 {c |}{res}          1        0.07       97.57
{txt}      14549 {c |}{res}          1        0.07       97.64
{txt}      14650 {c |}{res}          1        0.07       97.70
{txt}      14688 {c |}{res}          1        0.07       97.77
{txt}      15835 {c |}{res}          1        0.07       97.84
{txt}      16490 {c |}{res}          1        0.07       97.90
{txt}      17569 {c |}{res}          1        0.07       97.97
{txt}      24379 {c |}{res}          1        0.07       98.03
{txt}      24455 {c |}{res}          1        0.07       98.10
{txt}      26522 {c |}{res}          1        0.07       98.16
{txt}      33247 {c |}{res}          1        0.07       98.23
{txt}      34936 {c |}{res}          1        0.07       98.30
{txt}      38302 {c |}{res}          1        0.07       98.36
{txt}      43616 {c |}{res}          1        0.07       98.43
{txt}      49539 {c |}{res}          1        0.07       98.49
{txt}      54370 {c |}{res}          1        0.07       98.56
{txt}      65646 {c |}{res}          1        0.07       98.62
{txt}      79080 {c |}{res}          1        0.07       98.69
{txt}      87289 {c |}{res}          1        0.07       98.75
{txt}     100959 {c |}{res}          1        0.07       98.82
{txt}     106391 {c |}{res}          1        0.07       98.89
{txt}     110512 {c |}{res}          1        0.07       98.95
{txt}     134644 {c |}{res}          1        0.07       99.02
{txt}     140918 {c |}{res}          1        0.07       99.08
{txt}     142078 {c |}{res}          1        0.07       99.15
{txt}     142426 {c |}{res}          1        0.07       99.21
{txt}     144094 {c |}{res}          1        0.07       99.28
{txt}     148643 {c |}{res}          1        0.07       99.34
{txt}     149439 {c |}{res}          1        0.07       99.41
{txt}     164905 {c |}{res}          1        0.07       99.48
{txt}     176103 {c |}{res}          1        0.07       99.54
{txt}     197368 {c |}{res}          1        0.07       99.61
{txt}     203829 {c |}{res}          1        0.07       99.67
{txt}     266396 {c |}{res}          1        0.07       99.74
{txt}     267508 {c |}{res}          1        0.07       99.80
{txt}     431827 {c |}{res}          1        0.07       99.87
{txt}     439873 {c |}{res}          1        0.07       99.93
{txt}     508750 {c |}{res}          1        0.07      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,525      100.00
{txt}
{com}. gen speed=0
{txt}
{com}. replace speed=1 if durationinseconds<427
{txt}(134 real changes made)

{com}. 
. *Table 2
. ttest trustel, by(treat) 

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
       0 {c |}{res}{col 12}    808{col 22} 3.309406{col 34} .0678142{col 46}  1.92764{col 58} 3.176293{col 70} 3.442519
       {txt}1 {c |}{res}{col 12}    782{col 22} 3.164962{col 34} .0699425{col 46}  1.95589{col 58} 3.027664{col 70} 3.302259
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}  1,590{col 22} 3.238365{col 34} .0487104{col 46} 1.942318{col 58} 3.142821{col 70} 3.333908
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .1444443{col 34} .0973971{col 58}-.0465961{col 70} .3354847
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}0{txt}) - mean({res}1{txt})                                      t = {res}  1.4830
{txt}H0: diff = 0                                     Degrees of freedom = {res}    1588

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9309         {txt}Pr(|T| > |t|) = {res}0.1383          {txt}Pr(T > t) = {res}0.0691
{txt}
{com}. ttest trustel if speed==0, by(treat)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
       0 {c |}{res}{col 12}    779{col 22} 3.292683{col 34} .0689498{col 46}  1.92443{col 58} 3.157333{col 70} 3.428033
       {txt}1 {c |}{res}{col 12}    765{col 22} 3.150327{col 34} .0703883{col 46} 1.946843{col 58} 3.012149{col 70} 3.288504
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}  1,544{col 22}  3.22215{col 34} .0492763{col 46} 1.936249{col 58} 3.125495{col 70} 3.318806
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .1423561{col 34} .0985218{col 58}-.0508948{col 70} .3356071
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}0{txt}) - mean({res}1{txt})                                      t = {res}  1.4449
{txt}H0: diff = 0                                     Degrees of freedom = {res}    1542

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9257         {txt}Pr(|T| > |t|) = {res}0.1487          {txt}Pr(T > t) = {res}0.0743
{txt}
{com}. ttest trustel if exp1c!=1, by(treat)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
       0 {c |}{res}{col 12}    808{col 22} 3.309406{col 34} .0678142{col 46}  1.92764{col 58} 3.176293{col 70} 3.442519
       {txt}1 {c |}{res}{col 12}    447{col 22} 3.049217{col 34} .0889372{col 46} 1.880343{col 58} 2.874429{col 70} 3.224005
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}  1,255{col 22} 3.216733{col 34} .0540349{col 46}  1.91424{col 58} 3.110724{col 70} 3.322742
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .2601889{col 34} .1126443{col 58} .0391967{col 70} .4811812
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}0{txt}) - mean({res}1{txt})                                      t = {res}  2.3098
{txt}H0: diff = 0                                     Degrees of freedom = {res}    1253

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9895         {txt}Pr(|T| > |t|) = {res}0.0211          {txt}Pr(T > t) = {res}0.0105
{txt}
{com}. 
. *Figure 2
. * Quintiles of approval
. sum norm, detail

                            {txt}norm
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,662
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,662

{txt}50%    {res}      .25                      {txt}Mean          {res}  .292494
                        {txt}Largest       Std. dev.     {res} .2651371
{txt}75%    {res}       .5              1
{txt}90%    {res}     .625              1       {txt}Variance      {res} .0702977
{txt}95%    {res}      .75              1       {txt}Skewness      {res} .5513302
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 2.492829
{txt}
{com}.         sum norm if !missing(trustel, treat), detail

                            {txt}norm
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      1,590
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      1,590

{txt}50%    {res}      .25                      {txt}Mean          {res} .2893082
                        {txt}Largest       Std. dev.     {res} .2630104
{txt}75%    {res}       .5              1
{txt}90%    {res}     .625              1       {txt}Variance      {res} .0691745
{txt}95%    {res}      .75              1       {txt}Skewness      {res} .5603796
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 2.517642
{txt}
{com}.         sum norm if !missing(trustel, treat, exp1c), detail

                            {txt}norm
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        782
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}        782

{txt}50%    {res}      .25                      {txt}Mean          {res} .2920396
                        {txt}Largest       Std. dev.     {res} .2631979
{txt}75%    {res}       .5              1
{txt}90%    {res}     .625              1       {txt}Variance      {res} .0692731
{txt}95%    {res}      .75              1       {txt}Skewness      {res}  .553074
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 2.531654
{txt}
{com}.         // 25%=0, 50%=.25, 75%=.5, 99-100%=1
. fre norm
{res}
{txt}norm
{txt}{hline 13}{hline 1}{c TT}{hline 44}
{txt}        {txt}      {c |}      Freq.    Percent      Valid       Cum.
{txt}{hline 13}{hline 1}{c +}{hline 44}
{txt}Valid   0     {c |}{res}        540      31.60      32.49      32.49
{txt}        .125  {c |}{res}         83       4.86       4.99      37.48
{txt}        .25   {c |}{res}        328      19.19      19.74      57.22
{txt}        .375  {c |}{res}        165       9.65       9.93      67.15
{txt}        .5    {c |}{res}        319      18.67      19.19      86.34
{txt}        .625  {c |}{res}         66       3.86       3.97      90.31
{txt}        .75   {c |}{res}        110       6.44       6.62      96.93
{txt}        .875  {c |}{res}         19       1.11       1.14      98.07
{txt}        1     {c |}{res}         32       1.87       1.93     100.00
{txt}        Total {c |}{res}       1662      97.25     100.00           
{txt}Missing .     {c |}{res}         47       2.75                      
{txt}Total         {c |}{res}       1709     100.00                      
{txt}{hline 13}{hline 1}{c BT}{hline 44}

{com}.         recode norm (0=0 "25%")(.125/.25=1 "50%")(.375/.5=2 "75%")(.625/1=3 "100%"), gen(norm_percentiles)
{txt}(1,122 differences between {bf:norm} and {bf:norm_percentiles})

{com}.         lab var norm_percentiles "Approval of Vote Buying, Quintiles"
{txt}
{com}.         
. * figure
. reg trustel c.norm_percentiles##i.treat

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,590
{txt}{hline 13}{c +}{hline 34}   F(3, 1586)      = {res}     7.93
{txt}       Model {c |} {res} 88.5442039         3  29.5147346   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 5906.11554     1,586   3.7239064   {txt}R-squared       ={res}    0.0148
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0129
{txt}       Total {c |} {res} 5994.65975     1,589  3.77259896   {txt}Root MSE        =   {res} 1.9297

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                 trustel{col 26}{c |} Coefficient{col 38}  Std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}norm_percentiles {c |}{col 26}{res}{space 2} .1718848{col 38}{space 2} .0649902{col 49}{space 1}    2.64{col 58}{space 3}0.008{col 66}{space 4} .0444091{col 79}{space 3} .2993606
{txt}{space 17}1.treat {c |}{col 26}{res}{space 2}-.2469746{col 38}{space 2} .1493868{col 49}{space 1}   -1.65{col 58}{space 3}0.098{col 66}{space 4}-.5399909{col 79}{space 3} .0460417
{txt}{space 24} {c |}
treat#c.norm_percentiles {c |}
{space 22}1  {c |}{col 26}{res}{space 2} .0792696{col 38}{space 2} .0925055{col 49}{space 1}    0.86{col 58}{space 3}0.392{col 66}{space 4}-.1021764{col 79}{space 3} .2607156
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.100081{col 38}{space 2} .1042735{col 49}{space 1}   29.73{col 58}{space 3}0.000{col 66}{space 4} 2.895552{col 79}{space 3} 3.304609
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         margins, dydx(treat) at(norm_percentiles=(0 1 2 3))
{res}
{txt}{col 1}Conditional marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,590}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.treat}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:3}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.treat     {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.treat      {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.2469746{col 26}{space 2} .1493868{col 37}{space 1}   -1.65{col 46}{space 3}0.098{col 54}{space 4}-.5399909{col 67}{space 3} .0460417
{txt}{space 10}2  {c |}{col 14}{res}{space 2} -.167705{col 26}{space 2} .0991179{col 37}{space 1}   -1.69{col 46}{space 3}0.091{col 54}{space 4}-.3621209{col 67}{space 3} .0267109
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0884355{col 26}{space 2} .1201951{col 37}{space 1}   -0.74{col 46}{space 3}0.462{col 54}{space 4}-.3241934{col 67}{space 3} .1473225
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0091659{col 26}{space 2} .1902207{col 37}{space 1}   -0.05{col 46}{space 3}0.962{col 54}{space 4}-.3822763{col 67}{space 3} .3639446
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) ///
>                 ytitle("Effects on Trust in Elections") ///
>                 title("Full Sample")
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:norm_percentiles}{p_end}
{res}{txt}
{com}.         graph save Graph "fig2_full.gph", replace               
{res}{txt}file {bf:fig2_full.gph} saved

{com}. reg trustel c.norm_percentiles##i.treat if exp1c!=1 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,255
{txt}{hline 13}{c +}{hline 34}   F(3, 1251)      = {res}     7.17
{txt}       Model {c |} {res}  77.626585         3  25.8755283   {txt}Prob > F        ={res}    0.0001
{txt}    Residual {c |} {res} 4517.42202     1,251  3.61104878   {txt}R-squared       ={res}    0.0169
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0145
{txt}       Total {c |} {res} 4595.04861     1,254  3.66431308   {txt}Root MSE        =   {res} 1.9003

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                 trustel{col 26}{c |} Coefficient{col 38}  Std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}norm_percentiles {c |}{col 26}{res}{space 2} .1718848{col 38}{space 2} .0639978{col 49}{space 1}    2.69{col 58}{space 3}0.007{col 66}{space 4} .0463299{col 79}{space 3} .2974398
{txt}{space 17}1.treat {c |}{col 26}{res}{space 2}-.3801711{col 38}{space 2} .1755641{col 49}{space 1}   -2.17{col 58}{space 3}0.031{col 66}{space 4}-.7246037{col 79}{space 3}-.0357386
{txt}{space 24} {c |}
treat#c.norm_percentiles {c |}
{space 22}1  {c |}{col 26}{res}{space 2} .0886468{col 38}{space 2} .1083166{col 49}{space 1}    0.82{col 58}{space 3}0.413{col 66}{space 4}-.1238555{col 79}{space 3} .3011491
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.100081{col 38}{space 2} .1026813{col 49}{space 1}   30.19{col 58}{space 3}0.000{col 66}{space 4} 2.898634{col 79}{space 3} 3.301527
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         margins, dydx(treat) at(norm_percentiles=(0 1 2 3))
{res}
{txt}{col 1}Conditional marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,255}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.treat}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:3}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.treat     {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.treat      {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.3801711{col 26}{space 2} .1755641{col 37}{space 1}   -2.17{col 46}{space 3}0.031{col 54}{space 4}-.7246037{col 67}{space 3}-.0357386
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.2915244{col 26}{space 2}  .115213{col 37}{space 1}   -2.53{col 46}{space 3}0.012{col 54}{space 4}-.5175564{col 67}{space 3}-.0654923
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.2028776{col 26}{space 2} .1385291{col 37}{space 1}   -1.46{col 46}{space 3}0.143{col 54}{space 4}-.4746526{col 67}{space 3} .0688974
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.1142308{col 26}{space 2} .2203896{col 37}{space 1}   -0.52{col 46}{space 3}0.604{col 54}{space 4}-.5466048{col 67}{space 3} .3181432
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) ///
>                 ytitle("Effects on Trust in Elections") ///
>                 title("Unsaturated Sample")
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:norm_percentiles}{p_end}
{res}{txt}
{com}.         graph save Graph "fig2_unsat.gph", replace
{res}{txt}file {bf:fig2_unsat.gph} saved

{com}. graph combine fig2_full.gph fig2_unsat.gph, xsize(8) ysize(4) title("")
{res}{txt}
{com}.         graph save Graph "fig2.gph", replace
{res}{txt}file {bf:fig2.gph} saved

{com}.         
. * figure note
. reg trustel c.norm_percentiles##i.treat

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,590
{txt}{hline 13}{c +}{hline 34}   F(3, 1586)      = {res}     7.93
{txt}       Model {c |} {res} 88.5442039         3  29.5147346   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 5906.11554     1,586   3.7239064   {txt}R-squared       ={res}    0.0148
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0129
{txt}       Total {c |} {res} 5994.65975     1,589  3.77259896   {txt}Root MSE        =   {res} 1.9297

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                 trustel{col 26}{c |} Coefficient{col 38}  Std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}norm_percentiles {c |}{col 26}{res}{space 2} .1718848{col 38}{space 2} .0649902{col 49}{space 1}    2.64{col 58}{space 3}0.008{col 66}{space 4} .0444091{col 79}{space 3} .2993606
{txt}{space 17}1.treat {c |}{col 26}{res}{space 2}-.2469746{col 38}{space 2} .1493868{col 49}{space 1}   -1.65{col 58}{space 3}0.098{col 66}{space 4}-.5399909{col 79}{space 3} .0460417
{txt}{space 24} {c |}
treat#c.norm_percentiles {c |}
{space 22}1  {c |}{col 26}{res}{space 2} .0792696{col 38}{space 2} .0925055{col 49}{space 1}    0.86{col 58}{space 3}0.392{col 66}{space 4}-.1021764{col 79}{space 3} .2607156
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.100081{col 38}{space 2} .1042735{col 49}{space 1}   29.73{col 58}{space 3}0.000{col 66}{space 4} 2.895552{col 79}{space 3} 3.304609
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         margins, dydx(treat) at(norm_percentiles=(0 2)) post coefl
{res}
{txt}{col 1}Conditional marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,590}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.treat}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:2}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}      dy/dx{col 26}   Legend
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.treat     {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.treat      {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.2469746{col 26}{txt}  _b[1.treat:1bn._at]
{space 10}2  {c |}{col 14}{res}{space 2}-.0884355{col 26}{txt}  _b[1.treat:2._at]
{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         lincom _b[1.treat:2._at] - _b[1.treat:1bn._at] // 25 vs 75%

{p 0 7}{space 1}{text:( 1)}{space 1}{space 1}{res}- [1.treat]1bn._at + [1.treat]2._at = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .1585392{col 26}{space 2} .1850111{col 37}{space 1}    0.86{col 46}{space 3}0.392{col 54}{space 4}-.2043528{col 67}{space 3} .5214311
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. reg trustel c.norm_percentiles##i.treat if exp1c!=1 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,255
{txt}{hline 13}{c +}{hline 34}   F(3, 1251)      = {res}     7.17
{txt}       Model {c |} {res}  77.626585         3  25.8755283   {txt}Prob > F        ={res}    0.0001
{txt}    Residual {c |} {res} 4517.42202     1,251  3.61104878   {txt}R-squared       ={res}    0.0169
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0145
{txt}       Total {c |} {res} 4595.04861     1,254  3.66431308   {txt}Root MSE        =   {res} 1.9003

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                 trustel{col 26}{c |} Coefficient{col 38}  Std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}norm_percentiles {c |}{col 26}{res}{space 2} .1718848{col 38}{space 2} .0639978{col 49}{space 1}    2.69{col 58}{space 3}0.007{col 66}{space 4} .0463299{col 79}{space 3} .2974398
{txt}{space 17}1.treat {c |}{col 26}{res}{space 2}-.3801711{col 38}{space 2} .1755641{col 49}{space 1}   -2.17{col 58}{space 3}0.031{col 66}{space 4}-.7246037{col 79}{space 3}-.0357386
{txt}{space 24} {c |}
treat#c.norm_percentiles {c |}
{space 22}1  {c |}{col 26}{res}{space 2} .0886468{col 38}{space 2} .1083166{col 49}{space 1}    0.82{col 58}{space 3}0.413{col 66}{space 4}-.1238555{col 79}{space 3} .3011491
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.100081{col 38}{space 2} .1026813{col 49}{space 1}   30.19{col 58}{space 3}0.000{col 66}{space 4} 2.898634{col 79}{space 3} 3.301527
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         margins, dydx(treat) at(norm_percentiles=(0 2)) post coefl
{res}
{txt}{col 1}Conditional marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,255}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.treat}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:2}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}      dy/dx{col 26}   Legend
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.treat     {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.treat      {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.3801711{col 26}{txt}  _b[1.treat:1bn._at]
{space 10}2  {c |}{col 14}{res}{space 2}-.2028776{col 26}{txt}  _b[1.treat:2._at]
{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         lincom _b[1.treat:2._at] - _b[1.treat:1bn._at] // 25 vs 75%

{p 0 7}{space 1}{text:( 1)}{space 1}{space 1}{res}- [1.treat]1bn._at + [1.treat]2._at = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .1772935{col 26}{space 2} .2166333{col 37}{space 1}    0.82{col 46}{space 3}0.413{col 54}{space 4}-.2477111{col 67}{space 3} .6022982
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         
. ********************************************************* Appendix analyses for AB 
. 
. cd "../data"
{res}C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\data
{txt}
{com}. use "AB_cps.dta", clear
{txt}(All data are copyrighted by LAPOP. For more info, run the command note list)

{com}. preserve
{txt}
{com}.         do vdem.do // recoding vdem data sets for merge
{txt}
{com}. 
. use "V-Dem-CY-Core-v12.dta", clear
{txt}(V-Dem-CY-Core)

{com}. 
. *creating pais variable for matching (Belize not in v-dem)
. gen pais=.
{txt}(27,380 missing values generated)

{com}.         replace pais=1 if country_name=="Mexico"
{txt}(233 real changes made)

{com}.         replace pais=2 if country_name=="Guatemala"
{txt}(233 real changes made)

{com}.         replace pais=3 if country_name=="El Salvador"
{txt}(184 real changes made)

{com}.         replace pais=4 if country_name=="Honduras"
{txt}(184 real changes made)

{com}.         replace pais=5 if country_name=="Nicaragua"
{txt}(184 real changes made)

{com}.         replace pais=6 if country_name=="Costa Rica"
{txt}(184 real changes made)

{com}.         replace pais=7 if country_name=="Panama"
{txt}(119 real changes made)

{com}.         replace pais=8 if country_name=="Colombia"
{txt}(233 real changes made)

{com}.         replace pais=9 if country_name=="Ecuador"
{txt}(192 real changes made)

{com}.         replace pais=10 if country_name=="Bolivia"
{txt}(197 real changes made)

{com}.         replace pais=11 if country_name=="Peru"
{txt}(233 real changes made)

{com}.         replace pais=12 if country_name=="Paraguay"
{txt}(211 real changes made)

{com}.         replace pais=13 if country_name=="Chile"
{txt}(233 real changes made)

{com}.         replace pais=14 if country_name=="Uruguay"
{txt}(197 real changes made)

{com}.         replace pais=15 if country_name=="Brazil"
{txt}(233 real changes made)

{com}.         replace pais=16 if country_name=="Venezuela"
{txt}(223 real changes made)

{com}.         replace pais=17 if country_name=="Argentina"
{txt}(233 real changes made)

{com}.         replace pais=21 if country_name=="Dominican Republic"
{txt}(212 real changes made)

{com}.         replace pais=22 if country_name=="Haiti"
{txt}(233 real changes made)

{com}.         replace pais=23 if country_name=="Jamaica"
{txt}(122 real changes made)

{com}.         replace pais=24 if country_name=="Guyana"
{txt}(122 real changes made)

{com}. drop if missing(pais)
{txt}(23,185 observations deleted)

{com}. 
. *creating year-specific variables for clientelism
. sum v2elvotbuy v2dlencmps v2psprlnks //-3.5~3 

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}v2elvotbuy {c |}{res}        995   -.9803025    1.074247     -3.067      2.762
{txt}{space 2}v2dlencmps {c |}{res}      3,983   -.9147786    1.323926     -3.506      2.702
{txt}{space 2}v2psprlnks {c |}{res}      4,017   -.7930864    1.233557     -3.287      2.949
{txt}
{com}. tab country_name year if year>2010 & year<2015 & !missing(pais, v2xnp_client), mis // all years

                      {txt}{c |}                    Year
         Country name {c |}      2011       2012       2013       2014 {c |}     Total
{hline 22}{c +}{hline 44}{c +}{hline 10}
            Argentina {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}              Bolivia {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}               Brazil {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}                Chile {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}             Colombia {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}           Costa Rica {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}   Dominican Republic {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}              Ecuador {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}          El Salvador {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}            Guatemala {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}               Guyana {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}                Haiti {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}             Honduras {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}              Jamaica {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}               Mexico {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}            Nicaragua {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}               Panama {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}             Paraguay {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}                 Peru {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}              Uruguay {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}            Venezuela {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}{hline 22}{c +}{hline 44}{c +}{hline 10}
                Total {c |}{res}        21         21         21         21 {txt}{c |}{res}        84 
{txt}
{com}. tab country_name year if year>2010 & year<2015 & !missing(pais, v2dlencmps), mis // all years

                      {txt}{c |}                    Year
         Country name {c |}      2011       2012       2013       2014 {c |}     Total
{hline 22}{c +}{hline 44}{c +}{hline 10}
            Argentina {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}              Bolivia {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}               Brazil {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}                Chile {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}             Colombia {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}           Costa Rica {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}   Dominican Republic {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}              Ecuador {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}          El Salvador {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}            Guatemala {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}               Guyana {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}                Haiti {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}             Honduras {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}              Jamaica {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}               Mexico {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}            Nicaragua {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}               Panama {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}             Paraguay {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}                 Peru {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}              Uruguay {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}            Venezuela {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}{hline 22}{c +}{hline 44}{c +}{hline 10}
                Total {c |}{res}        21         21         21         21 {txt}{c |}{res}        84 
{txt}
{com}. tab country_name year if year>2010 & year<2015 & !missing(pais, v2psprlnks), mis // all years

                      {txt}{c |}                    Year
         Country name {c |}      2011       2012       2013       2014 {c |}     Total
{hline 22}{c +}{hline 44}{c +}{hline 10}
            Argentina {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}              Bolivia {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}               Brazil {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}                Chile {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}             Colombia {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}           Costa Rica {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}   Dominican Republic {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}              Ecuador {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}          El Salvador {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}            Guatemala {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}               Guyana {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}                Haiti {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}             Honduras {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}              Jamaica {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}               Mexico {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}            Nicaragua {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}               Panama {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}             Paraguay {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}                 Peru {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}              Uruguay {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}            Venezuela {c |}{res}         1          1          1          1 {txt}{c |}{res}         4 
{txt}{hline 22}{c +}{hline 44}{c +}{hline 10}
                Total {c |}{res}        21         21         21         21 {txt}{c |}{res}        84 
{txt}
{com}. tab country_name year if year>2010 & year<2015 & !missing(pais, v2elvotbuy), mis // only some years

                      {txt}{c |}                    Year
         Country name {c |}      2011       2012       2013       2014 {c |}     Total
{hline 22}{c +}{hline 44}{c +}{hline 10}
            Argentina {c |}{res}         1          0          1          0 {txt}{c |}{res}         2 
{txt}              Bolivia {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}               Brazil {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}                Chile {c |}{res}         0          0          1          0 {txt}{c |}{res}         1 
{txt}             Colombia {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}           Costa Rica {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}   Dominican Republic {c |}{res}         0          1          0          0 {txt}{c |}{res}         1 
{txt}              Ecuador {c |}{res}         0          0          1          0 {txt}{c |}{res}         1 
{txt}          El Salvador {c |}{res}         0          1          0          1 {txt}{c |}{res}         2 
{txt}            Guatemala {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}               Guyana {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}                Haiti {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}             Honduras {c |}{res}         0          0          1          0 {txt}{c |}{res}         1 
{txt}              Jamaica {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}               Mexico {c |}{res}         0          1          0          0 {txt}{c |}{res}         1 
{txt}            Nicaragua {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}               Panama {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}             Paraguay {c |}{res}         0          0          1          0 {txt}{c |}{res}         1 
{txt}                 Peru {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}              Uruguay {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}            Venezuela {c |}{res}         0          1          1          0 {txt}{c |}{res}         2 
{txt}{hline 22}{c +}{hline 44}{c +}{hline 10}
                Total {c |}{res}         7          4          6          7 {txt}{c |}{res}        24 
{txt}
{com}. 
. gen particular2014=.
{txt}(4,195 missing values generated)

{com}.                 replace particular2014=v2dlencmps if year==2014
{txt}(21 real changes made)

{com}.                 
. gen clientelist2014=.
{txt}(4,195 missing values generated)

{com}.                 replace clientelist2014=v2psprlnks if year==2014
{txt}(21 real changes made)

{com}.         
. gen vbuy2014=.
{txt}(4,195 missing values generated)

{com}.         tab country_name year if year>2010 & year<2015 & !missing(pais, v2elvotbuy)             

                      {txt}{c |}                    Year
         Country name {c |}      2011       2012       2013       2014 {c |}     Total
{hline 22}{c +}{hline 44}{c +}{hline 10}
            Argentina {c |}{res}         1          0          1          0 {txt}{c |}{res}         2 
{txt}              Bolivia {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}               Brazil {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}                Chile {c |}{res}         0          0          1          0 {txt}{c |}{res}         1 
{txt}             Colombia {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}           Costa Rica {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}   Dominican Republic {c |}{res}         0          1          0          0 {txt}{c |}{res}         1 
{txt}              Ecuador {c |}{res}         0          0          1          0 {txt}{c |}{res}         1 
{txt}          El Salvador {c |}{res}         0          1          0          1 {txt}{c |}{res}         2 
{txt}            Guatemala {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}               Guyana {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}                Haiti {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}             Honduras {c |}{res}         0          0          1          0 {txt}{c |}{res}         1 
{txt}              Jamaica {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}               Mexico {c |}{res}         0          1          0          0 {txt}{c |}{res}         1 
{txt}            Nicaragua {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}               Panama {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}             Paraguay {c |}{res}         0          0          1          0 {txt}{c |}{res}         1 
{txt}                 Peru {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}              Uruguay {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}            Venezuela {c |}{res}         0          1          1          0 {txt}{c |}{res}         2 
{txt}{hline 22}{c +}{hline 44}{c +}{hline 10}
                Total {c |}{res}         7          4          6          7 {txt}{c |}{res}        24 
{txt}
{com}.                 replace vbuy2014=v2elvotbuy if year==2014
{txt}(7 real changes made)

{com}.                 replace vbuy2014=v2elvotbuy if year==2013 & inlist(country_name, "Argentina", "Chile", "Ecuador", "Honduras", "Paraguay", "Venezuela")
{txt}(6 real changes made)

{com}.                 replace vbuy2014=v2elvotbuy if year==2012 & inlist(country_name, "Dominican Republic", "Chile", "Mexico")
{txt}(2 real changes made)

{com}.                 replace vbuy2014=v2elvotbuy if year==2011 & inlist(country_name, "Guatemala", "Guyana", "Haiti", "Jamaica", "Nicaragua", "Peru")
{txt}(6 real changes made)

{com}.                 
. rescale vbuy2014 1 0
{txt}(21 real changes made)

{com}. rescale particular2014 1 0
{txt}(21 real changes made)

{com}. rescale clientelist2014 1 0     
{txt}(21 real changes made)

{com}. 
. *creating year-specific variables for election irregularities
. sum v2elirreg v2elintim v2elpeace v2elfrfair // other voting irregularities, govt intimidation, electoral violence, free & fair elections (-4.6~2.7)

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}v2elirreg {c |}{res}        990   -.7995535    1.239652     -2.906      2.543
{txt}{space 3}v2elintim {c |}{res}        994   -.3790433    1.327444     -3.403      2.535
{txt}{space 3}v2elpeace {c |}{res}        993   -.4734079    1.398229     -4.548      2.322
{txt}{space 2}v2elfrfair {c |}{res}        992   -.3990736    1.348143     -3.358      2.742
{txt}
{com}. tab country_name year if year>2010 & year<2015 & !missing(pais, v2elirreg), mis // only some years 

                      {txt}{c |}                    Year
         Country name {c |}      2011       2012       2013       2014 {c |}     Total
{hline 22}{c +}{hline 44}{c +}{hline 10}
            Argentina {c |}{res}         1          0          1          0 {txt}{c |}{res}         2 
{txt}              Bolivia {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}               Brazil {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}                Chile {c |}{res}         0          0          1          0 {txt}{c |}{res}         1 
{txt}             Colombia {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}           Costa Rica {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}   Dominican Republic {c |}{res}         0          1          0          0 {txt}{c |}{res}         1 
{txt}              Ecuador {c |}{res}         0          0          1          0 {txt}{c |}{res}         1 
{txt}          El Salvador {c |}{res}         0          1          0          1 {txt}{c |}{res}         2 
{txt}            Guatemala {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}               Guyana {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}                Haiti {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}             Honduras {c |}{res}         0          0          1          0 {txt}{c |}{res}         1 
{txt}              Jamaica {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}               Mexico {c |}{res}         0          1          0          0 {txt}{c |}{res}         1 
{txt}            Nicaragua {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}               Panama {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}             Paraguay {c |}{res}         0          0          1          0 {txt}{c |}{res}         1 
{txt}                 Peru {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}              Uruguay {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}            Venezuela {c |}{res}         0          1          1          0 {txt}{c |}{res}         2 
{txt}{hline 22}{c +}{hline 44}{c +}{hline 10}
                Total {c |}{res}         7          4          6          7 {txt}{c |}{res}        24 
{txt}
{com}.         gen irreg2014=. 
{txt}(4,195 missing values generated)

{com}.                 replace irreg2014=v2elirreg if year==2014
{txt}(7 real changes made)

{com}.                 replace irreg2014=v2elirreg if year==2013 & inlist(country_name, "Argentina", "Chile", "Ecuador", "Honduras", "Paraguay", "Venezuela")
{txt}(6 real changes made)

{com}.                 replace irreg2014=v2elirreg if year==2012 & inlist(country_name, "Dominican Republic", "Chile", "Mexico")
{txt}(2 real changes made)

{com}.                 replace irreg2014=v2elirreg if year==2011 & inlist(country_name, "Guatemala", "Guyana", "Haiti", "Jamaica", "Nicaragua", "Peru")
{txt}(6 real changes made)

{com}. tab country_name year if year>2010 & year<2015 & !missing(pais, v2elintim), mis // some years 

                      {txt}{c |}                    Year
         Country name {c |}      2011       2012       2013       2014 {c |}     Total
{hline 22}{c +}{hline 44}{c +}{hline 10}
            Argentina {c |}{res}         1          0          1          0 {txt}{c |}{res}         2 
{txt}              Bolivia {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}               Brazil {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}                Chile {c |}{res}         0          0          1          0 {txt}{c |}{res}         1 
{txt}             Colombia {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}           Costa Rica {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}   Dominican Republic {c |}{res}         0          1          0          0 {txt}{c |}{res}         1 
{txt}              Ecuador {c |}{res}         0          0          1          0 {txt}{c |}{res}         1 
{txt}          El Salvador {c |}{res}         0          1          0          1 {txt}{c |}{res}         2 
{txt}            Guatemala {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}               Guyana {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}                Haiti {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}             Honduras {c |}{res}         0          0          1          0 {txt}{c |}{res}         1 
{txt}              Jamaica {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}               Mexico {c |}{res}         0          1          0          0 {txt}{c |}{res}         1 
{txt}            Nicaragua {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}               Panama {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}             Paraguay {c |}{res}         0          0          1          0 {txt}{c |}{res}         1 
{txt}                 Peru {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}              Uruguay {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}            Venezuela {c |}{res}         0          1          1          0 {txt}{c |}{res}         2 
{txt}{hline 22}{c +}{hline 44}{c +}{hline 10}
                Total {c |}{res}         7          4          6          7 {txt}{c |}{res}        24 
{txt}
{com}.         gen intimidate2014=.
{txt}(4,195 missing values generated)

{com}.                 replace intimidate2014=v2elintim if year==2014
{txt}(7 real changes made)

{com}.                 replace intimidate2014=v2elintim if year==2013 & inlist(country_name, "Argentina", "Chile", "Ecuador", "Honduras", "Paraguay", "Venezuela")
{txt}(6 real changes made)

{com}.                 replace intimidate2014=v2elintim if year==2012 & inlist(country_name, "Dominican Republic", "Chile", "Mexico")
{txt}(2 real changes made)

{com}.                 replace intimidate2014=v2elintim if year==2011 & inlist(country_name, "Guatemala", "Guyana", "Haiti", "Jamaica", "Nicaragua", "Peru")
{txt}(6 real changes made)

{com}. tab country_name year if year>2010 & year<2015 & !missing(pais, v2elpeace), mis // some years 

                      {txt}{c |}                    Year
         Country name {c |}      2011       2012       2013       2014 {c |}     Total
{hline 22}{c +}{hline 44}{c +}{hline 10}
            Argentina {c |}{res}         1          0          1          0 {txt}{c |}{res}         2 
{txt}              Bolivia {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}               Brazil {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}                Chile {c |}{res}         0          0          1          0 {txt}{c |}{res}         1 
{txt}             Colombia {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}           Costa Rica {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}   Dominican Republic {c |}{res}         0          1          0          0 {txt}{c |}{res}         1 
{txt}              Ecuador {c |}{res}         0          0          1          0 {txt}{c |}{res}         1 
{txt}          El Salvador {c |}{res}         0          1          0          1 {txt}{c |}{res}         2 
{txt}            Guatemala {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}               Guyana {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}                Haiti {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}             Honduras {c |}{res}         0          0          1          0 {txt}{c |}{res}         1 
{txt}              Jamaica {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}               Mexico {c |}{res}         0          1          0          0 {txt}{c |}{res}         1 
{txt}            Nicaragua {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}               Panama {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}             Paraguay {c |}{res}         0          0          1          0 {txt}{c |}{res}         1 
{txt}                 Peru {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}              Uruguay {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}            Venezuela {c |}{res}         0          1          1          0 {txt}{c |}{res}         2 
{txt}{hline 22}{c +}{hline 44}{c +}{hline 10}
                Total {c |}{res}         7          4          6          7 {txt}{c |}{res}        24 
{txt}
{com}.         gen violent2014=.
{txt}(4,195 missing values generated)

{com}.                 replace violent2014=v2elpeace if year==2014
{txt}(7 real changes made)

{com}.                 replace violent2014=v2elpeace if year==2013 & inlist(country_name, "Argentina", "Chile", "Ecuador", "Honduras", "Paraguay", "Venezuela")
{txt}(6 real changes made)

{com}.                 replace violent2014=v2elpeace if year==2012 & inlist(country_name, "Dominican Republic", "Chile", "Mexico")
{txt}(2 real changes made)

{com}.                 replace violent2014=v2elpeace if year==2011 & inlist(country_name, "Guatemala", "Guyana", "Haiti", "Jamaica", "Nicaragua", "Peru")
{txt}(6 real changes made)

{com}. tab country_name year if year>2010 & year<2015 & !missing(pais, v2elfrfair), mis // some years

                      {txt}{c |}                    Year
         Country name {c |}      2011       2012       2013       2014 {c |}     Total
{hline 22}{c +}{hline 44}{c +}{hline 10}
            Argentina {c |}{res}         1          0          1          0 {txt}{c |}{res}         2 
{txt}              Bolivia {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}               Brazil {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}                Chile {c |}{res}         0          0          1          0 {txt}{c |}{res}         1 
{txt}             Colombia {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}           Costa Rica {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}   Dominican Republic {c |}{res}         0          1          0          0 {txt}{c |}{res}         1 
{txt}              Ecuador {c |}{res}         0          0          1          0 {txt}{c |}{res}         1 
{txt}          El Salvador {c |}{res}         0          1          0          1 {txt}{c |}{res}         2 
{txt}            Guatemala {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}               Guyana {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}                Haiti {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}             Honduras {c |}{res}         0          0          1          0 {txt}{c |}{res}         1 
{txt}              Jamaica {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}               Mexico {c |}{res}         0          1          0          0 {txt}{c |}{res}         1 
{txt}            Nicaragua {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}               Panama {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}             Paraguay {c |}{res}         0          0          1          0 {txt}{c |}{res}         1 
{txt}                 Peru {c |}{res}         1          0          0          0 {txt}{c |}{res}         1 
{txt}              Uruguay {c |}{res}         0          0          0          1 {txt}{c |}{res}         1 
{txt}            Venezuela {c |}{res}         0          1          1          0 {txt}{c |}{res}         2 
{txt}{hline 22}{c +}{hline 44}{c +}{hline 10}
                Total {c |}{res}         7          4          6          7 {txt}{c |}{res}        24 
{txt}
{com}.         gen frfair2014=.
{txt}(4,195 missing values generated)

{com}.                 replace frfair2014=v2elfrfair if year==2014
{txt}(7 real changes made)

{com}.                 replace frfair2014=v2elfrfair if year==2013 & inlist(country_name, "Argentina", "Chile", "Ecuador", "Honduras", "Paraguay", "Venezuela")
{txt}(6 real changes made)

{com}.                 replace frfair2014=v2elfrfair if year==2012 & inlist(country_name, "Dominican Republic", "Chile", "Mexico")
{txt}(2 real changes made)

{com}.                 replace frfair2014=v2elfrfair if year==2011 & inlist(country_name, "Guatemala", "Guyana", "Haiti", "Jamaica", "Nicaragua", "Peru")
{txt}(6 real changes made)

{com}. rescale irreg2014 1 0
{txt}(21 real changes made)

{com}. rescale intimidate2014 1 0
{txt}(21 real changes made)

{com}. rescale violent2014 1 0
{txt}(21 real changes made)

{com}. rescale frfair2014 1 0
{txt}(21 real changes made)

{com}. 
. 
. drop if missing(vbuy2014) & missing(particular2014) & missing(clientelist2014) & missing(irreg2014) & missing(intimidate2014) & missing(violent2014) & missing(frfair2014)
{txt}(4,160 observations deleted)

{com}.                 
. 
. *creating dataset 
. keep country_name pais vbuy2014 particular2014 clientelist2014 irreg2014 intimidate2014 violent2014 frfair2014
{txt}
{com}. collapse vbuy2014 particular2014 clientelist2014 irreg2014 intimidate2014 violent2014 frfair2014, by(country_name pais)
{res}{txt}
{com}. gen wave=2014
{txt}
{com}. rename vbuy2014 vbuy
{res}{txt}
{com}.         rename particular2014 particular
{res}{txt}
{com}.         rename clientelist2014 clientelist
{res}{txt}
{com}.         rename irreg2014 irregularities
{res}{txt}
{com}.         rename intimidate2014 govtintimidation
{res}{txt}
{com}.         rename violent2014 elviolence
{res}{txt}
{com}.         rename frfair2014 freefair
{res}{txt}
{com}. lab var vbuy "Prevalence of Vote Buying (Vdem)"
{txt}
{com}. lab var particular "Prevalence of Particularistic Goods (Vdem)"
{txt}
{com}. lab var clientelist "Prevalence of Clientelistic Party Linkages (Vdem)"
{txt}
{com}. save "vdem.dta", replace
{txt}{p 0 4 2}
(file {bf}
vdem.dta{rm}
not found)
{p_end}
{p 0 4 2}
file {bf}
vdem.dta{rm}
saved
{p_end}

{com}. 
.         
. 
{txt}end of do-file

{com}. restore
{txt}
{com}. merge m:m pais wave using "vdem" // merging vdem data set
{res}{txt}{p 0 7 2}
(variable
{bf:pais} was {bf:byte}, now {bf:float} to accommodate using data's values)
{p_end}
{p 0 7 2}
(variable
{bf:wave} was {bf:int}, now {bf:float} to accommodate using data's values)
{p_end}

{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}         276,808
{txt}{col 9}from master{col 30}{res}         276,808{txt}  (_merge==1)
{col 9}from using{col 30}{res}               0{txt}  (_merge==2)

{col 5}Matched{col 30}{res}          33,446{txt}  (_merge==3)
{col 5}{hline 41}

{com}. cd "../figures and tables"
{res}C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables
{txt}
{com}. 
. ****Vote buying exposure
. *2010 (code any exposure to 1, and code no exposure to 0)
. recode clien1 (1/2=1)(3=0), gen(client3)
{txt}(41,930 differences between {bf:clien1} and {bf:client3})

{com}. lab var client3 "Vote Buying Target"
{txt}
{com}. *2014 direct
. recode clien1na (2=0), gen(client)
{txt}(47,955 differences between {bf:clien1na} and {bf:client})

{com}. lab var client "Vote Buying Target (direct measure)"
{txt}
{com}. *2014 indirect
. recode clien1n (2=0), gen(indirect)
{txt}(39,880 differences between {bf:clien1n} and {bf:indirect})

{com}. lab var indirect "Vote Buying Target (indirect)"
{txt}
{com}. 
. *combining direct vote buying exposure to range across years
. tab client3 wave // 2010-2

      {txt}Vote {c |}
    Buying {c |}      Survey Wave
    Target {c |}      2010       2012 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}    32,464      5,726 {txt}{c |}{res}    38,190 
{txt}         1 {c |}{res}     4,487      1,634 {txt}{c |}{res}     6,121 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}    36,951      7,360 {txt}{c |}{res}    44,311 
{txt}
{com}.         tab client wave // 2014~

      {txt}Vote {c |}
    Buying {c |}
    Target {c |}
   (direct {c |}           Survey Wave
  measure) {c |}      2014    2016/17    2018/19 {c |}     Total
{hline 11}{c +}{hline 33}{c +}{hline 10}
         0 {c |}{res}    31,723      4,185     12,047 {txt}{c |}{res}    47,955 
{txt}         1 {c |}{res}     2,826        435      1,898 {txt}{c |}{res}     5,159 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
     Total {c |}{res}    34,549      4,620     13,945 {txt}{c |}{res}    53,114 
{txt}
{com}.         tab indirect wave // same

      {txt}Vote {c |}
    Buying {c |}
    Target {c |}           Survey Wave
(indirect) {c |}      2014    2016/17    2018/19 {c |}     Total
{hline 11}{c +}{hline 33}{c +}{hline 10}
         0 {c |}{res}    29,778      1,064      9,038 {txt}{c |}{res}    39,880 
{txt}         1 {c |}{res}     4,492        413      3,110 {txt}{c |}{res}     8,015 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
     Total {c |}{res}    34,270      1,477     12,148 {txt}{c |}{res}    47,895 
{txt}
{com}. egen direct = rowtotal(client3 client)
{txt}
{com}. replace direct=. if client>1 & client3>1
{txt}(212,829 real changes made, 212,829 to missing)

{com}. lab var direct "Vote Buying Target (direct)"
{txt}
{com}. 
. ***combining trust in elections to range across years
. tab b47a b47 // no obs
{txt}no observations

{com}.         tab b47a wave // from 2012, other than ecuador

                      {txt}{c |}                      Survey Wave
   Trust in Elections {c |}      2004       2012       2014    2016/17    2018/19 {c |}     Total
{hline 22}{c +}{hline 55}{c +}{hline 10}
           Not at All {c |}{res}       181      4,504      6,625      7,035      5,964 {txt}{c |}{res}    24,309 
{txt}                    2 {c |}{res}       180      3,254      3,575      3,481      3,211 {txt}{c |}{res}    13,701 
{txt}                    3 {c |}{res}       304      5,415      5,439      4,511      3,988 {txt}{c |}{res}    19,657 
{txt}                    4 {c |}{res}       508      7,422      7,055      5,820      5,326 {txt}{c |}{res}    26,131 
{txt}                    5 {c |}{res}       662      7,882      6,370      5,879      5,165 {txt}{c |}{res}    25,958 
{txt}                    6 {c |}{res}       629      5,041      4,410      4,489      3,752 {txt}{c |}{res}    18,321 
{txt}                A Lot {c |}{res}       513      4,099      3,896      3,861      3,192 {txt}{c |}{res}    15,561 
{txt}{hline 22}{c +}{hline 55}{c +}{hline 10}
                Total {c |}{res}     2,977     37,617     37,370     35,076     30,598 {txt}{c |}{res}   143,638 
{txt}
{com}.                 tab b47a pais if wave==2004 // ecuador 

                      {txt}{c |}  Country
   Trust in Elections {c |}   Ecuador {c |}     Total
{hline 22}{c +}{hline 11}{c +}{hline 10}
           Not at All {c |}{res}       181 {txt}{c |}{res}       181 
{txt}                    2 {c |}{res}       180 {txt}{c |}{res}       180 
{txt}                    3 {c |}{res}       304 {txt}{c |}{res}       304 
{txt}                    4 {c |}{res}       508 {txt}{c |}{res}       508 
{txt}                    5 {c |}{res}       662 {txt}{c |}{res}       662 
{txt}                    6 {c |}{res}       629 {txt}{c |}{res}       629 
{txt}                A Lot {c |}{res}       513 {txt}{c |}{res}       513 
{txt}{hline 22}{c +}{hline 11}{c +}{hline 10}
                Total {c |}{res}     2,977 {txt}{c |}{res}     2,977 
{txt}
{com}.                 tab pais b47 if wave==2004 // ecuador not included

                      {txt}{c |}          �Hasta qu� punto tiene usted confianza en las elecciones?
              Country {c |}    1 Nada          2          3          4          5          6    7 Mucho {c |}     Total
{hline 22}{c +}{hline 77}{c +}{hline 10}
               Mexico {c |}{res}       178        113        168        271        330        284        180 {txt}{c |}{res}     1,524 
{txt}            Guatemala {c |}{res}       257        206        193        223        246        217        188 {txt}{c |}{res}     1,530 
{txt}          El Salvador {c |}{res}       185         77        113        145        273        323        436 {txt}{c |}{res}     1,552 
{txt}             Honduras {c |}{res}       329        184        199        231        186        124        153 {txt}{c |}{res}     1,406 
{txt}            Nicaragua {c |}{res}       305        141        137        172        220        206        184 {txt}{c |}{res}     1,365 
{txt}           Costa Rica {c |}{res}       123         74        114        165        293        362        357 {txt}{c |}{res}     1,488 
{txt}               Panama {c |}{res}       182         77        102        177        265        401        412 {txt}{c |}{res}     1,616 
{txt}             Colombia {c |}{res}       197        169        191        234        291        221        150 {txt}{c |}{res}     1,453 
{txt}{hline 22}{c +}{hline 77}{c +}{hline 10}
                Total {c |}{res}     1,756      1,041      1,217      1,618      2,104      2,138      2,060 {txt}{c |}{res}    11,934 
{txt}
{com}.         tab b47 wave // ~2010

     {txt}�Hasta qu� punto {c |}
tiene usted confianza {c |}                 Survey Wave
   en las elecciones? {c |}      2004       2006       2008       2010 {c |}     Total
{hline 22}{c +}{hline 44}{c +}{hline 10}
               1 Nada {c |}{res}     1,756      4,202      5,329      5,078 {txt}{c |}{res}    16,365 
{txt}                    2 {c |}{res}     1,041      2,273      3,369      3,471 {txt}{c |}{res}    10,154 
{txt}                    3 {c |}{res}     1,217      3,078      5,204      5,466 {txt}{c |}{res}    14,965 
{txt}                    4 {c |}{res}     1,618      3,859      7,337      7,301 {txt}{c |}{res}    20,115 
{txt}                    5 {c |}{res}     2,104      4,014      6,760      7,458 {txt}{c |}{res}    20,336 
{txt}                    6 {c |}{res}     2,138      3,002      4,812      5,257 {txt}{c |}{res}    15,209 
{txt}              7 Mucho {c |}{res}     2,060      2,658      4,259      4,389 {txt}{c |}{res}    13,366 
{txt}{hline 22}{c +}{hline 44}{c +}{hline 10}
                Total {c |}{res}    11,934     23,086     37,070     38,420 {txt}{c |}{res}   110,510 
{txt}
{com}. egen trustel = rowtotal(b47a b47)
{txt}
{com}.         replace trustel=. if missing(b47) & missing(b47a)
{txt}(56,106 real changes made, 56,106 to missing)

{com}.         tab trustel wave

           {txt}{c |}                                       Survey Wave
   trustel {c |}      2004       2006       2008       2010       2012       2014    2016/17    2018/19 {c |}     Total
{hline 11}{c +}{hline 88}{c +}{hline 10}
         1 {c |}{res}     1,937      4,202      5,329      5,078      4,504      6,625      7,035      5,964 {txt}{c |}{res}    40,674 
{txt}         2 {c |}{res}     1,221      2,273      3,369      3,471      3,254      3,575      3,481      3,211 {txt}{c |}{res}    23,855 
{txt}         3 {c |}{res}     1,521      3,078      5,204      5,466      5,415      5,439      4,511      3,988 {txt}{c |}{res}    34,622 
{txt}         4 {c |}{res}     2,126      3,859      7,337      7,301      7,422      7,055      5,820      5,326 {txt}{c |}{res}    46,246 
{txt}         5 {c |}{res}     2,766      4,014      6,760      7,458      7,882      6,370      5,879      5,165 {txt}{c |}{res}    46,294 
{txt}         6 {c |}{res}     2,767      3,002      4,812      5,257      5,041      4,410      4,489      3,752 {txt}{c |}{res}    33,530 
{txt}         7 {c |}{res}     2,573      2,658      4,259      4,389      4,099      3,896      3,861      3,192 {txt}{c |}{res}    28,927 
{txt}{hline 11}{c +}{hline 88}{c +}{hline 10}
     Total {c |}{res}    14,911     23,086     37,070     38,420     37,617     37,370     35,076     30,598 {txt}{c |}{res}   254,148 
{txt}
{com}. 
. ***approval of vote buying
. tab1 clien4 clien4a clien4b // all same qs

{res}-> tabulation of clien4  

   {txt}Agree with Giving Benefits for {c |}
                            Votes {c |}      Freq.     Percent        Cum.
{hline 34}{c +}{hline 35}
                Strongly approves {c |}{res}         59        3.96        3.96
{txt}                         Approves {c |}{res}        122        8.18       12.14
{txt}Does not approve, but understands {c |}{res}        353       23.68       35.81
{txt}                      Disapproves {c |}{res}        654       43.86       79.68
{txt}             Strongly disapproves {c |}{res}        303       20.32      100.00
{txt}{hline 34}{c +}{hline 35}
                            Total {c |}{res}      1,491      100.00

-> tabulation of clien4a  

   {txt}Agree with Giving Benefits for {c |}
                            Votes {c |}      Freq.     Percent        Cum.
{hline 34}{c +}{hline 35}
                Strongly approves {c |}{res}        259        5.80        5.80
{txt}                         Approves {c |}{res}        379        8.49       14.29
{txt}Does not approve, but understands {c |}{res}        983       22.02       36.30
{txt}                      Disapproves {c |}{res}      1,643       36.80       73.10
{txt}             Strongly disapproves {c |}{res}      1,201       26.90      100.00
{txt}{hline 34}{c +}{hline 35}
                            Total {c |}{res}      4,465      100.00

-> tabulation of clien4b  

   {txt}Agree with Giving Benefits for {c |}
                            Votes {c |}      Freq.     Percent        Cum.
{hline 34}{c +}{hline 35}
                Strongly approves {c |}{res}        282        6.31        6.31
{txt}                         Approves {c |}{res}        396        8.86       15.16
{txt}Does not approve, but understands {c |}{res}      1,006       22.50       37.66
{txt}                      Disapproves {c |}{res}      1,679       37.54       75.20
{txt}             Strongly disapproves {c |}{res}      1,109       24.80      100.00
{txt}{hline 34}{c +}{hline 35}
                            Total {c |}{res}      4,472      100.00
{txt}
{com}.         tab pais clien4 // nicaragua

                      {txt}{c |}          Agree with Giving Benefits for Votes
              Country {c |} Strongly    Approves  Does not   Disapprov  Strongly  {c |}     Total
{hline 22}{c +}{hline 55}{c +}{hline 10}
            Nicaragua {c |}{res}        59        122        353        654        303 {txt}{c |}{res}     1,491 
{txt}{hline 22}{c +}{hline 55}{c +}{hline 10}
                Total {c |}{res}        59        122        353        654        303 {txt}{c |}{res}     1,491 
{txt}
{com}.         tab pais clien4a // mexico, guatemala, peru, paraguay, dominican, jamaica

                      {txt}{c |}          Agree with Giving Benefits for Votes
              Country {c |} Strongly    Approves  Does not   Disapprov  Strongly  {c |}     Total
{hline 22}{c +}{hline 55}{c +}{hline 10}
               Mexico {c |}{res}        12         55        142        309        225 {txt}{c |}{res}       743 
{txt}            Guatemala {c |}{res}        30         79        177        299        177 {txt}{c |}{res}       762 
{txt}                 Peru {c |}{res}        19         56        128        297        255 {txt}{c |}{res}       755 
{txt}             Paraguay {c |}{res}        35         56        142        314        165 {txt}{c |}{res}       712 
{txt}   Dominican Republic {c |}{res}        84         79        216        207        158 {txt}{c |}{res}       744 
{txt}              Jamaica {c |}{res}        79         54        178        217        221 {txt}{c |}{res}       749 
{txt}{hline 22}{c +}{hline 55}{c +}{hline 10}
                Total {c |}{res}       259        379        983      1,643      1,201 {txt}{c |}{res}     4,465 
{txt}
{com}.         tab pais clien4b // same

                      {txt}{c |}          Agree with Giving Benefits for Votes
              Country {c |} Strongly    Approves  Does not   Disapprov  Strongly  {c |}     Total
{hline 22}{c +}{hline 55}{c +}{hline 10}
               Mexico {c |}{res}        21         61        132        350        247 {txt}{c |}{res}       811 
{txt}            Guatemala {c |}{res}        47         77        153        330        153 {txt}{c |}{res}       760 
{txt}                 Peru {c |}{res}        26         53        135        277        246 {txt}{c |}{res}       737 
{txt}             Paraguay {c |}{res}        37         75        189        302        150 {txt}{c |}{res}       753 
{txt}   Dominican Republic {c |}{res}        86         85        208        209        138 {txt}{c |}{res}       726 
{txt}              Jamaica {c |}{res}        65         45        189        211        175 {txt}{c |}{res}       685 
{txt}{hline 22}{c +}{hline 55}{c +}{hline 10}
                Total {c |}{res}       282        396      1,006      1,679      1,109 {txt}{c |}{res}     4,472 
{txt}
{com}.         tab clien4a clien4b // no obs
{txt}no observations

{com}.         tab clien4a clien4b, mis

    {txt}Agree with Giving {c |}                                Agree with Giving Benefits for Votes
   Benefits for Votes {c |} Strongly    Approves  Does not   Disapprov  Strongly   Don't Kno  No Respon  Not Appli  Not asked {c |}     Total
{hline 22}{c +}{hline 99}{c +}{hline 10}
    Strongly approves {c |}{res}         0          0          0          0          0          0          0        259          0 {txt}{c |}{res}       259 
{txt}             Approves {c |}{res}         0          0          0          0          0          0          0        379          0 {txt}{c |}{res}       379 
{txt}Does not approve, but {c |}{res}         0          0          0          0          0          0          0        983          0 {txt}{c |}{res}       983 
{txt}          Disapproves {c |}{res}         0          0          0          0          0          0          0      1,643          0 {txt}{c |}{res}     1,643 
{txt} Strongly disapproves {c |}{res}         0          0          0          0          0          0          0      1,201          0 {txt}{c |}{res}     1,201 
{txt}           Don't Know {c |}{res}         0          0          0          0          0          0          0         97          0 {txt}{c |}{res}        97 
{txt}          No Response {c |}{res}         0          0          0          0          0          0          0         57          0 {txt}{c |}{res}        57 
{txt}       Not Applicable {c |}{res}       282        396      1,006      1,679      1,109        105         45          0          0 {txt}{c |}{res}     4,622 
{txt}Not asked in this cou {c |}{res}         0          0          0          0          0          0          0          0    301,013 {txt}{c |}{res}   301,013 
{txt}{hline 22}{c +}{hline 99}{c +}{hline 10}
                Total {c |}{res}       282        396      1,006      1,679      1,109        105         45      4,619    301,013 {txt}{c |}{res}   310,254 
{txt}
{com}. egen approval=rowtotal(clien4 clien4a clien4b)
{txt}
{com}.         sum clien4 clien4a clien4b

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}clien4 {c |}{res}      1,491    3.684105    1.012079          1          5
{txt}{space 5}clien4a {c |}{res}      4,465    3.705039    1.124282          1          5
{txt}{space 5}clien4b {c |}{res}      4,472    3.656753    1.130108          1          5
{txt}
{com}.         tab approval // runs from strongly agree to strongly disagree

   {txt}approval {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}    299,826       96.64       96.64
{txt}          1 {c |}{res}        600        0.19       96.83
{txt}          2 {c |}{res}        897        0.29       97.12
{txt}          3 {c |}{res}      2,342        0.75       97.88
{txt}          4 {c |}{res}      3,976        1.28       99.16
{txt}          5 {c |}{res}      2,613        0.84      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}    310,254      100.00
{txt}
{com}.         recode approval (0=.)
{txt}(299,826 changes made to {bf:approval})

{com}.         rescale approval 1 0 // to run from strongly disagree(0) to strongly agree (1)
{txt}(9,828 real changes made)

{com}. 
. ***control variables
. recode q1 (1=0)(2=1)(3=0), gen(woman)
{txt}(310,222 differences between {bf:q1} and {bf:woman})

{com}.         tab woman q1, mis

 {txt}RECODE of {c |}                                     Sex
  q1 (Sex) {c |}      Male     Female      Other          .  Don't Kno  No Respon  Not Appli {c |}     Total
{hline 11}{c +}{hline 77}{c +}{hline 10}
         0 {c |}{res}   151,838          0         11          0          0          0          0 {txt}{c |}{res}   151,849 
{txt}         1 {c |}{res}         0    158,373          0          0          0          0          0 {txt}{c |}{res}   158,373 
{txt}         . {c |}{res}         0          0          0         15          0          0          0 {txt}{c |}{res}        15 
{txt}        .a {c |}{res}         0          0          0          0          3          0          0 {txt}{c |}{res}         3 
{txt}        .b {c |}{res}         0          0          0          0          0         13          0 {txt}{c |}{res}        13 
{txt}        .c {c |}{res}         0          0          0          0          0          0          1 {txt}{c |}{res}         1 
{txt}{hline 11}{c +}{hline 77}{c +}{hline 10}
     Total {c |}{res}   151,838    158,373         11         15          3         13          1 {txt}{c |}{res}   310,254 
{txt}
{com}. recode q2 (16/25=0)(26/35=1)(36/45=2)(46/55=3)(56/65=4)(66/112=5), gen(agecohort)
{txt}(307,627 differences between {bf:q2} and {bf:agecohort})

{com}. recode ur 1=0 2=1, gen(rural)
{txt}(290,476 differences between {bf:ur} and {bf:rural})

{com}.         label variable rural "Rural"
{txt}
{com}.         
. ****rescaling 
. rescale agecohort 0 1 
{txt}variable {bf}{res}agecohort{sf}{txt} was {bf}{res}int{sf}{txt} now {bf}{res}float{sf}
{txt}(240,303 real changes made, 2,610 to missing)

{com}.         lab def agecohort 0 "16-25" 1 "66+"
{txt}
{com}.         lab val agecohort agecohort
{txt}
{com}. rescale quintall 0 1
{txt}(281,230 real changes made)

{com}. rescale edr 0 1
{txt}(272,625 real changes made)

{com}. rescale m1 1 0 // presidential approval
{txt}variable {bf}{res}m1{sf}{txt} was {bf}{res}byte{sf}{txt} now {bf}{res}float{sf}
{txt}(286,834 real changes made, 15,515 to missing)

{com}. rescale ing4 0 1 // support for democracy
{txt}variable {bf}{res}ing4{sf}{txt} was {bf}{res}byte{sf}{txt} now {bf}{res}float{sf}
{txt}(310,254 real changes made, 20,902 to missing)

{com}. rescale exc7new 0 1 // perceived degree of corruption among politicians
{txt}variable {bf}{res}exc7new{sf}{txt} was {bf}{res}byte{sf}{txt} now {bf}{res}float{sf}
{txt}(310,254 real changes made, 262,469 to missing)

{com}. rescale idio2 0 1 // perception of personal econ situation, from better to worse
{txt}variable {bf}{res}idio2{sf}{txt} was {bf}{res}byte{sf}{txt} now {bf}{res}float{sf}
{txt}(310,254 real changes made, 26,664 to missing)

{com}. rescale soct2 0 1 // perception of country's econ situation, from better to worse
{txt}variable {bf}{res}soct2{sf}{txt} was {bf}{res}byte{sf}{txt} now {bf}{res}float{sf}
{txt}(310,254 real changes made, 26,398 to missing)

{com}. rescale psa5 0 1 // trust in institutions
{txt}(285,691 real changes made)

{com}. 
. *adding months from most recent election date
. gen months=.
{txt}(310,254 missing values generated)

{com}. replace months=6 if pais==1 & wave==2010
{txt}(1,562 real changes made)

{com}.         replace months=18 if pais==1 & wave==2014
{txt}(1,535 real changes made)

{com}.         replace months=6 if pais==1 & wave==2018
{txt}(1,580 real changes made)

{com}. replace months=27 if pais==2 & wave==2010
{txt}(1,494 real changes made)

{com}.         replace months=31 if pais==2 & wave==2014
{txt}(1,506 real changes made)

{com}.         replace months=37 if pais==2 & wave==2018
{txt}(1,596 real changes made)

{com}. replace months=11 if pais==3 & wave==2010
{txt}(1,550 real changes made)

{com}.         replace months=1 if pais==3 & wave==2014
{txt}(1,512 real changes made)

{com}.         replace months=7 if pais==3 & wave==2016
{txt}(1,551 real changes made)

{com}.         replace months=8 if pais==3 & wave==2018
{txt}(1,511 real changes made)

{com}. replace months=4 if pais==4 & wave==2014
{txt}(1,561 real changes made)

{com}.         replace months=11 if pais==4 & wave==2018
{txt}(1,560 real changes made)

{com}. replace months=39 if pais==5 & wave==2010
{txt}(1,540 real changes made)

{com}.         replace months=27 if pais==5 & wave==2014
{txt}(1,546 real changes made)

{com}.         replace months=56 if pais==5 & wave==2016
{txt}(1,560 real changes made)

{com}. replace months=0 if pais==6 & wave==2010
{txt}(1,500 real changes made)

{com}.         replace months=1 if pais==6 & wave==2014
{txt}(1,537 real changes made)

{com}. replace months=9 if pais==7 & wave==2010
{txt}(1,536 real changes made)

{com}.         replace months=58 if pais==7 & wave==2014
{txt}(1,508 real changes made)

{com}. replace months=45 if pais==8 & wave==2010
{txt}(1,506 real changes made)

{com}.         replace months=46 if pais==8 & wave==2014
{txt}(1,496 real changes made)

{com}.         replace months=3 if pais==8 & wave==2018
{txt}(1,663 real changes made)

{com}. replace months=10 if pais==9 & wave==2010
{txt}(2,999 real changes made)

{com}.         replace months=11 if pais==9 & wave==2014
{txt}(1,489 real changes made)

{com}. replace months=2 if pais==10 & wave==2010
{txt}(3,018 real changes made)

{com}.         replace months=52 if pais==10 & wave==2014
{txt}(3,066 real changes made)

{com}. replace months=43 if pais==11 & wave==2010
{txt}(1,500 real changes made)

{com}.         replace months=31 if pais==11 & wave==2014
{txt}(1,500 real changes made)

{com}.         replace months=32 if pais==11 & wave==2018
{txt}(1,521 real changes made)

{com}. replace months=21 if pais==12 & wave==2010
{txt}(1,501 real changes made)

{com}.         replace months=9 if pais==12 & wave==2014
{txt}(1,503 real changes made)

{com}.         replace months=42 if pais==12 & wave==2016
{txt}(1,528 real changes made)

{com}.         replace months=10 if pais==12 & wave==2018
{txt}(1,515 real changes made)

{com}. replace months=5 if pais==13 & wave==2010
{txt}(1,965 real changes made)

{com}.         replace months=4 if pais==13 & wave==2014
{txt}(1,571 real changes made)

{com}. replace months=4 if pais==14 & wave==2010
{txt}(1,500 real changes made)

{com}.         replace months=52 if pais==14 & wave==2014
{txt}(1,512 real changes made)

{com}. replace months=41 if pais==15 & wave==2010
{txt}(2,482 real changes made)

{com}.         replace months=41 if pais==15 & wave==2014
{txt}(1,500 real changes made)

{com}. replace months=37 if pais==16 & wave==2010
{txt}(1,500 real changes made)

{com}.         replace months=11 if pais==16 & wave==2014
{txt}(1,500 real changes made)

{com}. replace months=9 if pais==17 & wave==2010
{txt}(1,410 real changes made)

{com}.         replace months=5 if pais==17 & wave==2014
{txt}(1,512 real changes made)

{com}. replace months=20 if pais==21 & wave==2010
{txt}(1,498 real changes made)

{com}.         replace months=22 if pais==21 & wave==2014
{txt}(1,520 real changes made)

{com}.         replace months=35 if pais==21 & wave==2018
{txt}(1,516 real changes made)

{com}. replace months=35 if pais==22 & wave==2014
{txt}(1,512 real changes made)

{com}. replace months=29 if pais==23 & wave==2010
{txt}(1,504 real changes made)

{com}.         replace months=26 if pais==23 & wave==2014
{txt}(1,503 real changes made)

{com}.         replace months=36 if pais==23 & wave==2018
{txt}(1,513 real changes made)

{com}. replace months=41 if pais==24 & wave==2010
{txt}(1,540 real changes made)

{com}.         replace months=31 if pais==24 & wave==2014
{txt}(1,557 real changes made)

{com}. replace months=23 if pais==25 & wave==2010
{txt}(1,503 real changes made)

{com}. replace months=25 if pais==26 & wave==2010
{txt}(1,504 real changes made)

{com}.         replace months=26 if pais==26 & wave==2014      
{txt}(1,533 real changes made)

{com}.         
. ***A3: summary statistics
. gen vbmeasured=0
{txt}
{com}.         replace vbmeasured=1 if !missing(indirect) | !missing(direct)
{txt}(97,639 real changes made)

{com}.         tab indirect direct if vbmeasured==0, mis

           {txt}{c |}    Vote
      Vote {c |}   Buying
    Buying {c |}   Target
    Target {c |}  (direct)
(indirect) {c |}         . {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
        .a {c |}{res}       150 {txt}{c |}{res}       150 
{txt}        .b {c |}{res}       103 {txt}{c |}{res}       103 
{txt}        .z {c |}{res}   212,362 {txt}{c |}{res}   212,362 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}   212,615 {txt}{c |}{res}   212,615 
{txt}
{com}.         tab indirect direct if vbmeasured==1, mis

      {txt}Vote {c |}
    Buying {c |}
    Target {c |}   Vote Buying Target (direct)
(indirect) {c |}         0          1          . {c |}     Total
{hline 11}{c +}{hline 33}{c +}{hline 10}
         0 {c |}{res}    38,496      1,208        176 {txt}{c |}{res}    39,880 
{txt}         1 {c |}{res}     4,415      3,562         38 {txt}{c |}{res}     8,015 
{txt}        .a {c |}{res}       501         34          0 {txt}{c |}{res}       535 
{txt}        .b {c |}{res}       120         16          0 {txt}{c |}{res}       136 
{txt}        .z {c |}{res}    42,613      6,460          0 {txt}{c |}{res}    49,073 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
     Total {c |}{res}    86,145     11,280        214 {txt}{c |}{res}    97,639 
{txt}
{com}. sum trustel woman quintall agecohort edr rural m1 direct indirect ///
>         if !missing(trustel, woman, quintall, agecohort, edr, rural, m1) & vbmeasured==1 & wave==2014

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}trustel {c |}{res}     32,916    3.853384    1.934931          1          7
{txt}{space 7}woman {c |}{res}     32,916    .5097825    .4999119          0          1
{txt}{space 4}quintall {c |}{res}     32,916    .4916074    .3559724          0          1
{txt}{space 3}agecohort {c |}{res}     32,916    .3929578    .3098822          0          1
{txt}{space 9}edr {c |}{res}     32,916    .6188682    .2540218          0          1
{txt}{hline 13}{c +}{hline 57}
{space 7}rural {c |}{res}     32,916    .3231559    .4676888          0          1
{txt}{space 10}m1 {c |}{res}     32,916    .5662444    .2564409          0          1
{txt}{space 6}direct {c |}{res}     32,745    .0827913    .2755707          0          1
{txt}{space 4}indirect {c |}{res}     32,507    .1322177    .3387325          0          1
{txt}
{com}. 
. 
. ***A4: propensity matching for table 1
. teffects psmatch (trustel) (direct rural woman agecohort edr quintall i.pais m1) if wave==2014, nneighbor(3)
{res}
{txt}Treatment-effects estimation{col 48}Number of obs {col 67}= {res}    32,745
{txt:Estimator}{col 16}:{res: propensity-score matching}{col 48}{txt:Matches: requested }{col 67}{txt:=}          3
{txt:Outcome model}{col 16}:{res: matching}{txt}{col 63}min {col 67}= {res}         3
{txt:Treatment model}{col 16}:{res: logit}{col 63}{txt:max }{col 67}{txt:=}         18
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}   AI robust
{col 1}     trustel{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 6}direct {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2}-.1785893{col 26}{space 2} .0501998{col 37}{space 1}   -3.56{col 46}{space 3}0.000{col 54}{space 4}-.2769791{col 67}{space 3}-.0801994
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.                         outreg2 using a4.doc, dec(2) replace ctitle(Direct)
{txt}{stata `"shellout using `"a4.doc"'"':a4.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a4.txt""':seeout}

{com}. teffects psmatch (trustel) (indirect rural woman agecohort edr quintall i.pais m1) if wave==2014, nneighbor(3)
{res}
{txt}Treatment-effects estimation{col 48}Number of obs {col 67}= {res}    32,507
{txt:Estimator}{col 16}:{res: propensity-score matching}{col 48}{txt:Matches: requested }{col 67}{txt:=}          3
{txt:Outcome model}{col 16}:{res: matching}{txt}{col 63}min {col 67}= {res}         3
{txt:Treatment model}{col 16}:{res: logit}{col 63}{txt:max }{col 67}{txt:=}         21
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}   AI robust
{col 1}     trustel{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 4}indirect {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2}-.1420797{col 26}{space 2} .0392167{col 37}{space 1}   -3.62{col 46}{space 3}0.000{col 54}{space 4} -.218943{col 67}{space 3}-.0652164
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.                         outreg2 using a4.doc, dec(2) append ctitle(Indirect)
{txt}{stata `"shellout using `"a4.doc"'"':a4.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a4.txt""':seeout}

{com}.                         
. 
. ***A5: showing balance for table 1 matching
. ebalance direct woman quintall agecohort edr rural i.pais if wave==2014, tar(3) gen(directwt)
{txt}note: 1b.pais omitted because of collinearity
{res}

Data Setup
{txt}Treatment variable:   {res}direct
{txt}Covariate adjustment:{res} woman quintall agecohort edr rural 2.pais 3.pais 4.pais 5.pais 6.pais 7.pais 8.pais 9.pais 10.pais 11.pais 12.pais 13.pais 14.pais 15.pais 16.pais 17.pais 21.pais 22.pais 23.pais 24.pais 26.pais {txt}(1st order).{res} woman quintall agecohort edr rural 2.pais 3.pais 4.pais 5.pais 6.pais 7.pais 8.pais 9.pais 10.pais 11.pais 12.pais 13.pais 14.pais 15.pais 16.pais 17.pais 21.pais 22.pais 23.pais 24.pais 26.pais {txt}(2nd order).{res}{res} woman quintall agecohort edr rural 2.pais 3.pais 4.pais 5.pais 6.pais 7.pais 8.pais 9.pais 10.pais 11.pais 12.pais 13.pais 14.pais 15.pais 16.pais 17.pais 21.pais 22.pais 23.pais 24.pais 26.pais {txt}(3rd order).


{res}Optimizing...
{txt}Iteration 1: Max Difference = {res}145799.136{txt}
{txt}Iteration 2: Max Difference = {res}53636.2763{txt}
{txt}Iteration 3: Max Difference = {res}19731.4551{txt}
{txt}Iteration 4: Max Difference = {res}7258.5684{txt}
{txt}Iteration 5: Max Difference = {res}2670.04985{txt}
{txt}Iteration 6: Max Difference = {res}982.028301{txt}
{txt}Iteration 7: Max Difference = {res}361.040104{txt}
{txt}Iteration 8: Max Difference = {res}132.59193{txt}
{txt}Iteration 9: Max Difference = {res}48.5521598{txt}
{txt}Iteration 10: Max Difference = {res}17.6396563{txt}
{txt}Iteration 11: Max Difference = {res}6.27615851{txt}
{txt}Iteration 12: Max Difference = {res}2.11010501{txt}
{txt}Iteration 13: Max Difference = {res}.603175827{txt}
{txt}Iteration 14: Max Difference = {res}.109081531{txt}
{txt}Iteration 15: Max Difference = {res}.006100976{txt}
{txt}maximum difference smaller than the tolerance level; {res}convergence achieved


Treated units: {txt}2777{col 24}{res}total of weights: {txt}2777
{res}Control units: {txt}31004{col 24}{res}total of weights: {txt}2777


{res}Before: {txt}without weighting
{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treat}{space 1}{c |}{space 1}{rcenter 31:Control}{space 1}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:mean}{space 1}{space 1}{ralign 9:variance}{space 1}{space 1}{ralign 9:skewness}{space 1}{c |}{space 1}{ralign 9:mean}{space 1}{space 1}{ralign 9:variance}{space 1}{space 1}{ralign 9:skewness}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 10}
{space 0}{space 0}{ralign 12:woman}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .4523}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2478}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1917}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .5184}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2497}}}{space 1}{space 1}{ralign 9:{res:{sf:  -.07359}}}{space 1}
{space 0}{space 0}{ralign 12:quintall}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     .484}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1311}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04559}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .4897}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1264}}}{space 1}{space 1}{ralign 9:{res:{sf:   .02977}}}{space 1}
{space 0}{space 0}{ralign 12:agecohort}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3651}}}{space 1}{space 1}{ralign 9:{res:{sf:    .0792}}}{space 1}{space 1}{ralign 9:{res:{sf:    .4854}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3988}}}{space 1}{space 1}{ralign 9:{res:{sf:   .09806}}}{space 1}{space 1}{ralign 9:{res:{sf:    .3939}}}{space 1}
{space 0}{space 0}{ralign 12:edr}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .6038}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06673}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.2041}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .6178}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06479}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.1855}}}{space 1}
{space 0}{space 0}{ralign 12:rural}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3633}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2314}}}{space 1}{space 1}{ralign 9:{res:{sf:    .5683}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3203}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2177}}}{space 1}{space 1}{ralign 9:{res:{sf:    .7704}}}{space 1}
{space 0}{space 0}{ralign 12:2.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .06554}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06127}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.511}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04083}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03917}}}{space 1}{space 1}{ralign 9:{res:{sf:     4.64}}}{space 1}
{space 0}{space 0}{ralign 12:3.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .02197}}}{space 1}{space 1}{ralign 9:{res:{sf:   .02149}}}{space 1}{space 1}{ralign 9:{res:{sf:    6.523}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04641}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04426}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.312}}}{space 1}
{space 0}{space 0}{ralign 12:4.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .1001}}}{space 1}{space 1}{ralign 9:{res:{sf:   .09012}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.665}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04003}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03843}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.693}}}{space 1}
{space 0}{space 0}{ralign 12:5.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03061}}}{space 1}{space 1}{ralign 9:{res:{sf:   .02968}}}{space 1}{space 1}{ralign 9:{res:{sf:     5.45}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .0468}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04461}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.291}}}{space 1}
{space 0}{space 0}{ralign 12:6.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01152}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01139}}}{space 1}{space 1}{ralign 9:{res:{sf:    9.154}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04645}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04429}}}{space 1}{space 1}{ralign 9:{res:{sf:     4.31}}}{space 1}
{space 0}{space 0}{ralign 12:7.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03169}}}{space 1}{space 1}{ralign 9:{res:{sf:    .0307}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.347}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04419}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04224}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.436}}}{space 1}
{space 0}{space 0}{ralign 12:8.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04105}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03938}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.626}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04328}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04141}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.489}}}{space 1}
{space 0}{space 0}{ralign 12:9.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04537}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04333}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.369}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04158}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03985}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.593}}}{space 1}
{space 0}{space 0}{ralign 12:10.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .05798}}}{space 1}{space 1}{ralign 9:{res:{sf:   .05463}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.783}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .08989}}}{space 1}{space 1}{ralign 9:{res:{sf:   .08181}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.868}}}{space 1}
{space 0}{space 0}{ralign 12:11.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03061}}}{space 1}{space 1}{ralign 9:{res:{sf:   .02968}}}{space 1}{space 1}{ralign 9:{res:{sf:     5.45}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04412}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04218}}}{space 1}{space 1}{ralign 9:{res:{sf:     4.44}}}{space 1}
{space 0}{space 0}{ralign 12:12.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .06482}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06064}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.535}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04109}}}{space 1}{space 1}{ralign 9:{res:{sf:    .0394}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.624}}}{space 1}
{space 0}{space 0}{ralign 12:13.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  .008642}}}{space 1}{space 1}{ralign 9:{res:{sf:  .008571}}}{space 1}{space 1}{ralign 9:{res:{sf:    10.62}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04703}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04482}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.279}}}{space 1}
{space 0}{space 0}{ralign 12:14.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01801}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01769}}}{space 1}{space 1}{ralign 9:{res:{sf:     7.25}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .0468}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04461}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.291}}}{space 1}
{space 0}{space 0}{ralign 12:15.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .0569}}}{space 1}{space 1}{ralign 9:{res:{sf:   .05368}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.826}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04274}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04091}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.521}}}{space 1}
{space 0}{space 0}{ralign 12:16.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .0144}}}{space 1}{space 1}{ralign 9:{res:{sf:    .0142}}}{space 1}{space 1}{ralign 9:{res:{sf:    8.151}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04538}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04332}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.368}}}{space 1}
{space 0}{space 0}{ralign 12:17.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01188}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01175}}}{space 1}{space 1}{ralign 9:{res:{sf:    9.009}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04393}}}{space 1}{space 1}{ralign 9:{res:{sf:     .042}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.451}}}{space 1}
{space 0}{space 0}{ralign 12:21.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .1311}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1139}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.186}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03638}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03506}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.952}}}{space 1}
{space 0}{space 0}{ralign 12:22.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04609}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04398}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.329}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03945}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03789}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.732}}}{space 1}
{space 0}{space 0}{ralign 12:23.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03205}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03103}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.314}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04422}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04227}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.434}}}{space 1}
{space 0}{space 0}{ralign 12:24.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01152}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01139}}}{space 1}{space 1}{ralign 9:{res:{sf:    9.154}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .0478}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04552}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.239}}}{space 1}
{space 0}{space 0}{ralign 12:26.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .08822}}}{space 1}{space 1}{ralign 9:{res:{sf:   .08047}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.904}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04103}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03935}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.628}}}{space 1}


{res}After:  {txt}directwt as the weighting variable
{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treat}{space 1}{c |}{space 1}{rcenter 31:Control}{space 1}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:mean}{space 1}{space 1}{ralign 9:variance}{space 1}{space 1}{ralign 9:skewness}{space 1}{c |}{space 1}{ralign 9:mean}{space 1}{space 1}{ralign 9:variance}{space 1}{space 1}{ralign 9:skewness}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 10}
{space 0}{space 0}{ralign 12:woman}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .4523}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2478}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1917}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .4523}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2477}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1915}}}{space 1}
{space 0}{space 0}{ralign 12:quintall}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     .484}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1311}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04559}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     .484}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1311}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04562}}}{space 1}
{space 0}{space 0}{ralign 12:agecohort}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3651}}}{space 1}{space 1}{ralign 9:{res:{sf:    .0792}}}{space 1}{space 1}{ralign 9:{res:{sf:    .4854}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3651}}}{space 1}{space 1}{ralign 9:{res:{sf:    .0792}}}{space 1}{space 1}{ralign 9:{res:{sf:    .4854}}}{space 1}
{space 0}{space 0}{ralign 12:edr}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .6038}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06673}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.2041}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .6038}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06673}}}{space 1}{space 1}{ralign 9:{res:{sf:    -.204}}}{space 1}
{space 0}{space 0}{ralign 12:rural}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3633}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2314}}}{space 1}{space 1}{ralign 9:{res:{sf:    .5683}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3634}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2314}}}{space 1}{space 1}{ralign 9:{res:{sf:    .5679}}}{space 1}
{space 0}{space 0}{ralign 12:2.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .06554}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06127}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.511}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .06555}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06125}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.511}}}{space 1}
{space 0}{space 0}{ralign 12:3.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .02197}}}{space 1}{space 1}{ralign 9:{res:{sf:   .02149}}}{space 1}{space 1}{ralign 9:{res:{sf:    6.523}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .02197}}}{space 1}{space 1}{ralign 9:{res:{sf:   .02149}}}{space 1}{space 1}{ralign 9:{res:{sf:    6.522}}}{space 1}
{space 0}{space 0}{ralign 12:4.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .1001}}}{space 1}{space 1}{ralign 9:{res:{sf:   .09012}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.665}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .1001}}}{space 1}{space 1}{ralign 9:{res:{sf:   .09011}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.664}}}{space 1}
{space 0}{space 0}{ralign 12:5.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03061}}}{space 1}{space 1}{ralign 9:{res:{sf:   .02968}}}{space 1}{space 1}{ralign 9:{res:{sf:     5.45}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03061}}}{space 1}{space 1}{ralign 9:{res:{sf:   .02968}}}{space 1}{space 1}{ralign 9:{res:{sf:     5.45}}}{space 1}
{space 0}{space 0}{ralign 12:6.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01152}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01139}}}{space 1}{space 1}{ralign 9:{res:{sf:    9.154}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01155}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01141}}}{space 1}{space 1}{ralign 9:{res:{sf:    9.145}}}{space 1}
{space 0}{space 0}{ralign 12:7.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03169}}}{space 1}{space 1}{ralign 9:{res:{sf:    .0307}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.347}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03169}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03069}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.347}}}{space 1}
{space 0}{space 0}{ralign 12:8.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04105}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03938}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.626}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04106}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03937}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.626}}}{space 1}
{space 0}{space 0}{ralign 12:9.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04537}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04333}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.369}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04538}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04332}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.369}}}{space 1}
{space 0}{space 0}{ralign 12:10.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .05798}}}{space 1}{space 1}{ralign 9:{res:{sf:   .05463}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.783}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .05798}}}{space 1}{space 1}{ralign 9:{res:{sf:   .05462}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.783}}}{space 1}
{space 0}{space 0}{ralign 12:11.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03061}}}{space 1}{space 1}{ralign 9:{res:{sf:   .02968}}}{space 1}{space 1}{ralign 9:{res:{sf:     5.45}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03061}}}{space 1}{space 1}{ralign 9:{res:{sf:   .02968}}}{space 1}{space 1}{ralign 9:{res:{sf:     5.45}}}{space 1}
{space 0}{space 0}{ralign 12:12.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .06482}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06064}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.535}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .06483}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06062}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.535}}}{space 1}
{space 0}{space 0}{ralign 12:13.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  .008642}}}{space 1}{space 1}{ralign 9:{res:{sf:  .008571}}}{space 1}{space 1}{ralign 9:{res:{sf:    10.62}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  .008702}}}{space 1}{space 1}{ralign 9:{res:{sf:  .008627}}}{space 1}{space 1}{ralign 9:{res:{sf:    10.58}}}{space 1}
{space 0}{space 0}{ralign 12:14.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01801}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01769}}}{space 1}{space 1}{ralign 9:{res:{sf:     7.25}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01801}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01769}}}{space 1}{space 1}{ralign 9:{res:{sf:    7.249}}}{space 1}
{space 0}{space 0}{ralign 12:15.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .0569}}}{space 1}{space 1}{ralign 9:{res:{sf:   .05368}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.826}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .0569}}}{space 1}{space 1}{ralign 9:{res:{sf:   .05367}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.825}}}{space 1}
{space 0}{space 0}{ralign 12:16.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .0144}}}{space 1}{space 1}{ralign 9:{res:{sf:    .0142}}}{space 1}{space 1}{ralign 9:{res:{sf:    8.151}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01441}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01421}}}{space 1}{space 1}{ralign 9:{res:{sf:    8.148}}}{space 1}
{space 0}{space 0}{ralign 12:17.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01188}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01175}}}{space 1}{space 1}{ralign 9:{res:{sf:    9.009}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .0119}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01176}}}{space 1}{space 1}{ralign 9:{res:{sf:    9.003}}}{space 1}
{space 0}{space 0}{ralign 12:21.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .1311}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1139}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.186}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .1311}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1139}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.186}}}{space 1}
{space 0}{space 0}{ralign 12:22.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04609}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04398}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.329}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .0461}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04397}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.329}}}{space 1}
{space 0}{space 0}{ralign 12:23.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03205}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03103}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.314}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03205}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03103}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.313}}}{space 1}
{space 0}{space 0}{ralign 12:24.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01152}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01139}}}{space 1}{space 1}{ralign 9:{res:{sf:    9.154}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01155}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01142}}}{space 1}{space 1}{ralign 9:{res:{sf:    9.144}}}{space 1}
{space 0}{space 0}{ralign 12:26.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .08822}}}{space 1}{space 1}{ralign 9:{res:{sf:   .08047}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.904}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .08823}}}{space 1}{space 1}{ralign 9:{res:{sf:   .08045}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.904}}}{space 1}
{res}{txt}
{com}.         ebalance indirect woman quintall agecohort edr rural i.pais if wave==2014, tar(3) gen(indirectwt)
{txt}note: 1b.pais omitted because of collinearity
{res}

Data Setup
{txt}Treatment variable:   {res}indirect
{txt}Covariate adjustment:{res} woman quintall agecohort edr rural 2.pais 3.pais 4.pais 5.pais 6.pais 7.pais 8.pais 9.pais 10.pais 11.pais 12.pais 13.pais 14.pais 15.pais 16.pais 17.pais 21.pais 22.pais 23.pais 24.pais 26.pais {txt}(1st order).{res} woman quintall agecohort edr rural 2.pais 3.pais 4.pais 5.pais 6.pais 7.pais 8.pais 9.pais 10.pais 11.pais 12.pais 13.pais 14.pais 15.pais 16.pais 17.pais 21.pais 22.pais 23.pais 24.pais 26.pais {txt}(2nd order).{res}{res} woman quintall agecohort edr rural 2.pais 3.pais 4.pais 5.pais 6.pais 7.pais 8.pais 9.pais 10.pais 11.pais 12.pais 13.pais 14.pais 15.pais 16.pais 17.pais 21.pais 22.pais 23.pais 24.pais 26.pais {txt}(3rd order).


{res}Optimizing...
{txt}Iteration 1: Max Difference = {res}142899.07{txt}
{txt}Iteration 2: Max Difference = {res}52569.3841{txt}
{txt}Iteration 3: Max Difference = {res}19338.9499{txt}
{txt}Iteration 4: Max Difference = {res}7114.15632{txt}
{txt}Iteration 5: Max Difference = {res}2616.90613{txt}
{txt}Iteration 6: Max Difference = {res}962.460351{txt}
{txt}Iteration 7: Max Difference = {res}353.824043{txt}
{txt}Iteration 8: Max Difference = {res}129.920014{txt}
{txt}Iteration 9: Max Difference = {res}47.5523029{txt}
{txt}Iteration 10: Max Difference = {res}17.2675737{txt}
{txt}Iteration 11: Max Difference = {res}6.14583471{txt}
{txt}Iteration 12: Max Difference = {res}2.06770697{txt}
{txt}Iteration 13: Max Difference = {res}.591652257{txt}
{txt}Iteration 14: Max Difference = {res}.107368499{txt}
{txt}Iteration 15: Max Difference = {res}.005983245{txt}
{txt}maximum difference smaller than the tolerance level; {res}convergence achieved


Treated units: {txt}4407{col 24}{res}total of weights: {txt}4407
{res}Control units: {txt}29104{col 24}{res}total of weights: {txt}4407


{res}Before: {txt}without weighting
{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treat}{space 1}{c |}{space 1}{rcenter 31:Control}{space 1}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:mean}{space 1}{space 1}{ralign 9:variance}{space 1}{space 1}{ralign 9:skewness}{space 1}{c |}{space 1}{ralign 9:mean}{space 1}{space 1}{ralign 9:variance}{space 1}{space 1}{ralign 9:skewness}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 10}
{space 0}{space 0}{ralign 12:woman}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .4702}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2492}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1196}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     .519}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2496}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.0762}}}{space 1}
{space 0}{space 0}{ralign 12:quintall}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .4995}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1276}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.0211}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .4878}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1266}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03824}}}{space 1}
{space 0}{space 0}{ralign 12:agecohort}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3462}}}{space 1}{space 1}{ralign 9:{res:{sf:   .08108}}}{space 1}{space 1}{ralign 9:{res:{sf:    .5509}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .4024}}}{space 1}{space 1}{ralign 9:{res:{sf:   .09858}}}{space 1}{space 1}{ralign 9:{res:{sf:    .3812}}}{space 1}
{space 0}{space 0}{ralign 12:edr}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .6226}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06393}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.2587}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .6165}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06486}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.1747}}}{space 1}
{space 0}{space 0}{ralign 12:rural}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     .346}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2263}}}{space 1}{space 1}{ralign 9:{res:{sf:    .6473}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3194}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2174}}}{space 1}{space 1}{ralign 9:{res:{sf:    .7747}}}{space 1}
{space 0}{space 0}{ralign 12:2.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04652}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04436}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.307}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04226}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04048}}}{space 1}{space 1}{ralign 9:{res:{sf:     4.55}}}{space 1}
{space 0}{space 0}{ralign 12:3.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03835}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03689}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.808}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04535}}}{space 1}{space 1}{ralign 9:{res:{sf:    .0433}}}{space 1}{space 1}{ralign 9:{res:{sf:     4.37}}}{space 1}
{space 0}{space 0}{ralign 12:4.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .07556}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06987}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.212}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04085}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03919}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.639}}}{space 1}
{space 0}{space 0}{ralign 12:5.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03177}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03077}}}{space 1}{space 1}{ralign 9:{res:{sf:     5.34}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04776}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04548}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.241}}}{space 1}
{space 0}{space 0}{ralign 12:6.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  .008396}}}{space 1}{space 1}{ralign 9:{res:{sf:  .008327}}}{space 1}{space 1}{ralign 9:{res:{sf:    10.78}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04903}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04663}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.177}}}{space 1}
{space 0}{space 0}{ralign 12:7.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .02768}}}{space 1}{space 1}{ralign 9:{res:{sf:   .02692}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.758}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04577}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04367}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.347}}}{space 1}
{space 0}{space 0}{ralign 12:8.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04833}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04601}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.212}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04292}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04107}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.511}}}{space 1}
{space 0}{space 0}{ralign 12:9.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03199}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03098}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.319}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04377}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04186}}}{space 1}{space 1}{ralign 9:{res:{sf:     4.46}}}{space 1}
{space 0}{space 0}{ralign 12:10.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03789}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03647}}}{space 1}{space 1}{ralign 9:{res:{sf:     4.84}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .09555}}}{space 1}{space 1}{ralign 9:{res:{sf:   .08643}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.752}}}{space 1}
{space 0}{space 0}{ralign 12:11.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03018}}}{space 1}{space 1}{ralign 9:{res:{sf:   .02928}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.492}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04535}}}{space 1}{space 1}{ralign 9:{res:{sf:    .0433}}}{space 1}{space 1}{ralign 9:{res:{sf:     4.37}}}{space 1}
{space 0}{space 0}{ralign 12:12.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .07102}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06599}}}{space 1}{space 1}{ralign 9:{res:{sf:     3.34}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03879}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03729}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.777}}}{space 1}
{space 0}{space 0}{ralign 12:13.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  .009303}}}{space 1}{space 1}{ralign 9:{res:{sf:  .009219}}}{space 1}{space 1}{ralign 9:{res:{sf:    10.22}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .0491}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04669}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.174}}}{space 1}
{space 0}{space 0}{ralign 12:14.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03404}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03289}}}{space 1}{space 1}{ralign 9:{res:{sf:     5.14}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04628}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04414}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.319}}}{space 1}
{space 0}{space 0}{ralign 12:15.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .07284}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06755}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.287}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03986}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03827}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.704}}}{space 1}
{space 0}{space 0}{ralign 12:16.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01293}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01277}}}{space 1}{space 1}{ralign 9:{res:{sf:    8.621}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04773}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04545}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.243}}}{space 1}
{space 0}{space 0}{ralign 12:17.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01974}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01936}}}{space 1}{space 1}{ralign 9:{res:{sf:    6.905}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04494}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04292}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.393}}}{space 1}
{space 0}{space 0}{ralign 12:21.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .1062}}}{space 1}{space 1}{ralign 9:{res:{sf:   .09494}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.556}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03484}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03363}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.073}}}{space 1}
{space 0}{space 0}{ralign 12:22.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .06127}}}{space 1}{space 1}{ralign 9:{res:{sf:   .05753}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.659}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03285}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03177}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.242}}}{space 1}
{space 0}{space 0}{ralign 12:23.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04447}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04251}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.419}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04223}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04045}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.552}}}{space 1}
{space 0}{space 0}{ralign 12:24.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01793}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01761}}}{space 1}{space 1}{ralign 9:{res:{sf:    7.267}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     .049}}}{space 1}{space 1}{ralign 9:{res:{sf:    .0466}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.179}}}{space 1}
{space 0}{space 0}{ralign 12:26.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .09803}}}{space 1}{space 1}{ralign 9:{res:{sf:   .08844}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.704}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03663}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03529}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.934}}}{space 1}


{res}After:  {txt}indirectwt as the weighting variable
{res}
{txt}{space 0}{space 13}{c |}{res}{txt}{space 1}{rcenter 31:Treat}{space 1}{c |}{space 1}{rcenter 31:Control}{space 1}
{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:mean}{space 1}{space 1}{ralign 9:variance}{space 1}{space 1}{ralign 9:skewness}{space 1}{c |}{space 1}{ralign 9:mean}{space 1}{space 1}{ralign 9:variance}{space 1}{space 1}{ralign 9:skewness}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{c   +}{hline 11}{hline 11}{hline 10}
{space 0}{space 0}{ralign 12:woman}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .4702}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2492}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1196}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .4702}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2491}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1195}}}{space 1}
{space 0}{space 0}{ralign 12:quintall}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .4995}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1276}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.0211}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .4995}}}{space 1}{space 1}{ralign 9:{res:{sf:    .1276}}}{space 1}{space 1}{ralign 9:{res:{sf:  -.02106}}}{space 1}
{space 0}{space 0}{ralign 12:agecohort}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3462}}}{space 1}{space 1}{ralign 9:{res:{sf:   .08108}}}{space 1}{space 1}{ralign 9:{res:{sf:    .5509}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3462}}}{space 1}{space 1}{ralign 9:{res:{sf:   .08108}}}{space 1}{space 1}{ralign 9:{res:{sf:    .5509}}}{space 1}
{space 0}{space 0}{ralign 12:edr}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .6226}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06393}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.2587}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .6226}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06393}}}{space 1}{space 1}{ralign 9:{res:{sf:   -.2586}}}{space 1}
{space 0}{space 0}{ralign 12:rural}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     .346}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2263}}}{space 1}{space 1}{ralign 9:{res:{sf:    .6473}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .3461}}}{space 1}{space 1}{ralign 9:{res:{sf:    .2263}}}{space 1}{space 1}{ralign 9:{res:{sf:    .6471}}}{space 1}
{space 0}{space 0}{ralign 12:2.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04652}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04436}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.307}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04652}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04436}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.307}}}{space 1}
{space 0}{space 0}{ralign 12:3.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03835}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03689}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.808}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03835}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03688}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.808}}}{space 1}
{space 0}{space 0}{ralign 12:4.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .07556}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06987}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.212}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .07556}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06986}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.212}}}{space 1}
{space 0}{space 0}{ralign 12:5.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03177}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03077}}}{space 1}{space 1}{ralign 9:{res:{sf:     5.34}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03177}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03076}}}{space 1}{space 1}{ralign 9:{res:{sf:     5.34}}}{space 1}
{space 0}{space 0}{ralign 12:6.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  .008396}}}{space 1}{space 1}{ralign 9:{res:{sf:  .008327}}}{space 1}{space 1}{ralign 9:{res:{sf:    10.78}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  .008454}}}{space 1}{space 1}{ralign 9:{res:{sf:  .008383}}}{space 1}{space 1}{ralign 9:{res:{sf:    10.74}}}{space 1}
{space 0}{space 0}{ralign 12:7.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .02768}}}{space 1}{space 1}{ralign 9:{res:{sf:   .02692}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.758}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .02768}}}{space 1}{space 1}{ralign 9:{res:{sf:   .02692}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.758}}}{space 1}
{space 0}{space 0}{ralign 12:8.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04833}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04601}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.212}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04833}}}{space 1}{space 1}{ralign 9:{res:{sf:     .046}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.212}}}{space 1}
{space 0}{space 0}{ralign 12:9.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03199}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03098}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.319}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:     .032}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03097}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.319}}}{space 1}
{space 0}{space 0}{ralign 12:10.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03789}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03647}}}{space 1}{space 1}{ralign 9:{res:{sf:     4.84}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .0379}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03646}}}{space 1}{space 1}{ralign 9:{res:{sf:     4.84}}}{space 1}
{space 0}{space 0}{ralign 12:11.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03018}}}{space 1}{space 1}{ralign 9:{res:{sf:   .02928}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.492}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03018}}}{space 1}{space 1}{ralign 9:{res:{sf:   .02927}}}{space 1}{space 1}{ralign 9:{res:{sf:    5.492}}}{space 1}
{space 0}{space 0}{ralign 12:12.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .07102}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06599}}}{space 1}{space 1}{ralign 9:{res:{sf:     3.34}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .07102}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06598}}}{space 1}{space 1}{ralign 9:{res:{sf:     3.34}}}{space 1}
{space 0}{space 0}{ralign 12:13.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  .009303}}}{space 1}{space 1}{ralign 9:{res:{sf:  .009219}}}{space 1}{space 1}{ralign 9:{res:{sf:    10.22}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:  .009347}}}{space 1}{space 1}{ralign 9:{res:{sf:   .00926}}}{space 1}{space 1}{ralign 9:{res:{sf:     10.2}}}{space 1}
{space 0}{space 0}{ralign 12:14.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03404}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03289}}}{space 1}{space 1}{ralign 9:{res:{sf:     5.14}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .03404}}}{space 1}{space 1}{ralign 9:{res:{sf:   .03288}}}{space 1}{space 1}{ralign 9:{res:{sf:     5.14}}}{space 1}
{space 0}{space 0}{ralign 12:15.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .07284}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06755}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.287}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .07284}}}{space 1}{space 1}{ralign 9:{res:{sf:   .06754}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.287}}}{space 1}
{space 0}{space 0}{ralign 12:16.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01293}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01277}}}{space 1}{space 1}{ralign 9:{res:{sf:    8.621}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01294}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01278}}}{space 1}{space 1}{ralign 9:{res:{sf:    8.618}}}{space 1}
{space 0}{space 0}{ralign 12:17.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01974}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01936}}}{space 1}{space 1}{ralign 9:{res:{sf:    6.905}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01974}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01935}}}{space 1}{space 1}{ralign 9:{res:{sf:    6.904}}}{space 1}
{space 0}{space 0}{ralign 12:21.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .1062}}}{space 1}{space 1}{ralign 9:{res:{sf:   .09494}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.556}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:    .1062}}}{space 1}{space 1}{ralign 9:{res:{sf:   .09493}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.556}}}{space 1}
{space 0}{space 0}{ralign 12:22.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .06127}}}{space 1}{space 1}{ralign 9:{res:{sf:   .05753}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.659}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .06127}}}{space 1}{space 1}{ralign 9:{res:{sf:   .05752}}}{space 1}{space 1}{ralign 9:{res:{sf:    3.659}}}{space 1}
{space 0}{space 0}{ralign 12:23.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04447}}}{space 1}{space 1}{ralign 9:{res:{sf:   .04251}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.419}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .04448}}}{space 1}{space 1}{ralign 9:{res:{sf:    .0425}}}{space 1}{space 1}{ralign 9:{res:{sf:    4.419}}}{space 1}
{space 0}{space 0}{ralign 12:24.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01793}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01761}}}{space 1}{space 1}{ralign 9:{res:{sf:    7.267}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .01793}}}{space 1}{space 1}{ralign 9:{res:{sf:   .01761}}}{space 1}{space 1}{ralign 9:{res:{sf:    7.266}}}{space 1}
{space 0}{space 0}{ralign 12:26.pais}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .09803}}}{space 1}{space 1}{ralign 9:{res:{sf:   .08844}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.704}}}{space 1}{c |}{space 1}{ralign 9:{res:{sf:   .09803}}}{space 1}{space 1}{ralign 9:{res:{sf:   .08842}}}{space 1}{space 1}{ralign 9:{res:{sf:    2.704}}}{space 1}
{res}{txt}
{com}. sum direct indirect if wave==2014

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}direct {c |}{res}     34,549    .0817969     .274059          0          1
{txt}{space 4}indirect {c |}{res}     34,270    .1310767    .3374892          0          1
{txt}
{com}.         sum direct [aweight=directwt] if wave==2014

{txt}    Variable {c |}     Obs      Weight        Mean   Std. dev.       Min        Max
{hline 13}{c +}{hline 65}
{space 6}direct {c |}{res}  33,781        5554          .5   .5000074          0          1
{txt}
{com}.         sum indirect [aweight=indirectwt] if wave==2014

{txt}    Variable {c |}     Obs      Weight        Mean   Std. dev.       Min        Max
{hline 13}{c +}{hline 65}
{space 4}indirect {c |}{res}  33,511        8814          .5   .5000075          0          1
{txt}
{com}. 
. 
. ***A6: table 1 analysis for other years (direct exposure)
. svy: reg trustel direct woman quintall agecohort edr rural m1 i.pais i.wave 
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:320}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:90,216}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:4,807}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:83,545.342}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:4,487}
{txt}{col 51}{lalign 15:F({res:33}, {res:4455})}{col 66} = {res}{ralign 10:407.59}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.1742}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}            trustel{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}direct {c |}{col 21}{res}{space 2}-.1716622{col 33}{space 2} .0205584{col 44}{space 1}   -8.35{col 53}{space 3}0.000{col 61}{space 4}-.2119667{col 74}{space 3}-.1313576
{txt}{space 14}woman {c |}{col 21}{res}{space 2}-.0731269{col 33}{space 2} .0117404{col 44}{space 1}   -6.23{col 53}{space 3}0.000{col 61}{space 4}-.0961439{col 74}{space 3}-.0501099
{txt}{space 11}quintall {c |}{col 21}{res}{space 2} .0255534{col 33}{space 2} .0208547{col 44}{space 1}    1.23{col 53}{space 3}0.221{col 61}{space 4} -.015332{col 74}{space 3} .0664388
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2}   .26387{col 33}{space 2} .0218218{col 44}{space 1}   12.09{col 53}{space 3}0.000{col 61}{space 4} .2210886{col 74}{space 3} .3066515
{txt}{space 16}edr {c |}{col 21}{res}{space 2}-.0034734{col 33}{space 2} .0305415{col 44}{space 1}   -0.11{col 53}{space 3}0.909{col 61}{space 4}-.0633499{col 74}{space 3} .0564031
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .1670206{col 33}{space 2} .0186844{col 44}{space 1}    8.94{col 53}{space 3}0.000{col 61}{space 4}   .13039{col 74}{space 3} .2036513
{txt}{space 17}m1 {c |}{col 21}{res}{space 2} 2.386397{col 33}{space 2} .0301189{col 44}{space 1}   79.23{col 53}{space 3}0.000{col 61}{space 4} 2.327349{col 74}{space 3} 2.445445
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2}-.0852428{col 33}{space 2} .0457756{col 44}{space 1}   -1.86{col 53}{space 3}0.063{col 61}{space 4}-.1749855{col 74}{space 3} .0044998
{txt}{space 7}El Salvador  {c |}{col 21}{res}{space 2} .2058762{col 33}{space 2}  .042358{col 44}{space 1}    4.86{col 53}{space 3}0.000{col 61}{space 4} .1228335{col 74}{space 3} .2889188
{txt}{space 10}Honduras  {c |}{col 21}{res}{space 2}-.5737984{col 33}{space 2} .0495526{col 44}{space 1}  -11.58{col 53}{space 3}0.000{col 61}{space 4} -.670946{col 74}{space 3}-.4766508
{txt}{space 9}Nicaragua  {c |}{col 21}{res}{space 2}-.0854736{col 33}{space 2} .0504414{col 44}{space 1}   -1.69{col 53}{space 3}0.090{col 61}{space 4}-.1843637{col 74}{space 3} .0134164
{txt}{space 8}Costa Rica  {c |}{col 21}{res}{space 2} .8640713{col 33}{space 2} .0693328{col 44}{space 1}   12.46{col 53}{space 3}0.000{col 61}{space 4} .7281448{col 74}{space 3} .9999978
{txt}{space 12}Panama  {c |}{col 21}{res}{space 2} .3891812{col 33}{space 2} .0621877{col 44}{space 1}    6.26{col 53}{space 3}0.000{col 61}{space 4} .2672626{col 74}{space 3} .5110998
{txt}{space 10}Colombia  {c |}{col 21}{res}{space 2}-.3902655{col 33}{space 2} .0463259{col 44}{space 1}   -8.42{col 53}{space 3}0.000{col 61}{space 4}-.4810872{col 74}{space 3}-.2994439
{txt}{space 11}Ecuador  {c |}{col 21}{res}{space 2} .1395293{col 33}{space 2} .0592158{col 44}{space 1}    2.36{col 53}{space 3}0.019{col 61}{space 4} .0234373{col 74}{space 3} .2556214
{txt}{space 11}Bolivia  {c |}{col 21}{res}{space 2} .1940307{col 33}{space 2} .0511161{col 44}{space 1}    3.80{col 53}{space 3}0.000{col 61}{space 4} .0938179{col 74}{space 3} .2942435
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2} .1546426{col 33}{space 2} .0466847{col 44}{space 1}    3.31{col 53}{space 3}0.001{col 61}{space 4} .0631176{col 74}{space 3} .2461677
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2}-.2076509{col 33}{space 2} .0443302{col 44}{space 1}   -4.68{col 53}{space 3}0.000{col 61}{space 4}  -.29456{col 74}{space 3}-.1207418
{txt}{space 13}Chile  {c |}{col 21}{res}{space 2} .9776487{col 33}{space 2} .0566812{col 44}{space 1}   17.25{col 53}{space 3}0.000{col 61}{space 4} .8665256{col 74}{space 3} 1.088772
{txt}{space 11}Uruguay  {c |}{col 21}{res}{space 2} 1.579194{col 33}{space 2} .0535119{col 44}{space 1}   29.51{col 53}{space 3}0.000{col 61}{space 4} 1.474285{col 74}{space 3} 1.684104
{txt}{space 12}Brazil  {c |}{col 21}{res}{space 2}-.4092899{col 33}{space 2} .0620865{col 44}{space 1}   -6.59{col 53}{space 3}0.000{col 61}{space 4}-.5310101{col 74}{space 3}-.2875696
{txt}{space 9}Venezuela  {c |}{col 21}{res}{space 2} .3730896{col 33}{space 2} .0653899{col 44}{space 1}    5.71{col 53}{space 3}0.000{col 61}{space 4} .2448932{col 74}{space 3}  .501286
{txt}{space 9}Argentina  {c |}{col 21}{res}{space 2} .3233601{col 33}{space 2} .0575836{col 44}{space 1}    5.62{col 53}{space 3}0.000{col 61}{space 4} .2104679{col 74}{space 3} .4362523
{txt}Dominican Republic  {c |}{col 21}{res}{space 2} -.211829{col 33}{space 2}   .04397{col 44}{space 1}   -4.82{col 53}{space 3}0.000{col 61}{space 4}-.2980318{col 74}{space 3}-.1256262
{txt}{space 13}Haiti  {c |}{col 21}{res}{space 2}-1.312122{col 33}{space 2} .0792309{col 44}{space 1}  -16.56{col 53}{space 3}0.000{col 61}{space 4}-1.467453{col 74}{space 3} -1.15679
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2}-.2968367{col 33}{space 2} .0530197{col 44}{space 1}   -5.60{col 53}{space 3}0.000{col 61}{space 4}-.4007815{col 74}{space 3} -.192892
{txt}{space 12}Guyana  {c |}{col 21}{res}{space 2}-.1838654{col 33}{space 2}  .074544{col 44}{space 1}   -2.47{col 53}{space 3}0.014{col 61}{space 4}-.3300083{col 74}{space 3}-.0377225
{txt}{space 1}Trinidad & Tobago  {c |}{col 21}{res}{space 2} .0071822{col 33}{space 2} .0728616{col 44}{space 1}    0.10{col 53}{space 3}0.921{col 61}{space 4}-.1356624{col 74}{space 3} .1500268
{txt}{space 12}Belize  {c |}{col 21}{res}{space 2} .1668254{col 33}{space 2} .0611438{col 44}{space 1}    2.73{col 53}{space 3}0.006{col 61}{space 4} .0469534{col 74}{space 3} .2866973
{txt}{space 19} {c |}
{space 15}wave {c |}
{space 14}2012  {c |}{col 21}{res}{space 2} .1152938{col 33}{space 2} .0347684{col 44}{space 1}    3.32{col 53}{space 3}0.001{col 61}{space 4} .0471307{col 74}{space 3} .1834569
{txt}{space 14}2014  {c |}{col 21}{res}{space 2}-.2570345{col 33}{space 2} .0218074{col 44}{space 1}  -11.79{col 53}{space 3}0.000{col 61}{space 4}-.2997877{col 74}{space 3}-.2142813
{txt}{space 11}2016/17  {c |}{col 21}{res}{space 2}-.0919524{col 33}{space 2} .0374854{col 44}{space 1}   -2.45{col 53}{space 3}0.014{col 61}{space 4}-.1654422{col 74}{space 3}-.0184625
{txt}{space 11}2018/19  {c |}{col 21}{res}{space 2}-.3584154{col 33}{space 2}  .026049{col 44}{space 1}  -13.76{col 53}{space 3}0.000{col 61}{space 4}-.4094843{col 74}{space 3}-.3073464
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} 2.566872{col 33}{space 2} .0469074{col 44}{space 1}   54.72{col 53}{space 3}0.000{col 61}{space 4}  2.47491{col 74}{space 3} 2.658833
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using a6.doc, dec(2) replace ctitle(Pooled) drop(i.pais)
{txt}{stata `"shellout using `"a6.doc"'"':a6.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a6.txt""':seeout}

{com}. svy: reg trustel direct woman quintall agecohort edr rural m1 i.pais if wave==2010
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:110}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:33,353}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:1,872}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:29,149.673}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,762}
{txt}{col 51}{lalign 15:F({res:27}, {res:1736})}{col 66} = {res}{ralign 10:140.71}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.1653}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}            trustel{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}direct {c |}{col 21}{res}{space 2}-.0519922{col 33}{space 2}  .033997{col 44}{space 1}   -1.53{col 53}{space 3}0.126{col 61}{space 4}-.1186708{col 74}{space 3} .0146864
{txt}{space 14}woman {c |}{col 21}{res}{space 2}-.0775607{col 33}{space 2} .0193815{col 44}{space 1}   -4.00{col 53}{space 3}0.000{col 61}{space 4}-.1155738{col 74}{space 3}-.0395475
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}  .089677{col 33}{space 2} .0360747{col 44}{space 1}    2.49{col 53}{space 3}0.013{col 61}{space 4} .0189232{col 74}{space 3} .1604308
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2} .2573823{col 33}{space 2} .0374534{col 44}{space 1}    6.87{col 53}{space 3}0.000{col 61}{space 4} .1839245{col 74}{space 3}   .33084
{txt}{space 16}edr {c |}{col 21}{res}{space 2} .0508656{col 33}{space 2} .0505751{col 44}{space 1}    1.01{col 53}{space 3}0.315{col 61}{space 4}-.0483278{col 74}{space 3} .1500591
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .0905477{col 33}{space 2} .0360934{col 44}{space 1}    2.51{col 53}{space 3}0.012{col 61}{space 4} .0197572{col 74}{space 3} .1613381
{txt}{space 17}m1 {c |}{col 21}{res}{space 2} 2.276784{col 33}{space 2} .0600367{col 44}{space 1}   37.92{col 53}{space 3}0.000{col 61}{space 4} 2.159033{col 74}{space 3} 2.394534
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2}-.0851912{col 33}{space 2} .0853053{col 44}{space 1}   -1.00{col 53}{space 3}0.318{col 61}{space 4}-.2525014{col 74}{space 3} .0821191
{txt}{space 7}El Salvador  {c |}{col 21}{res}{space 2} .2126566{col 33}{space 2} .0766987{col 44}{space 1}    2.77{col 53}{space 3}0.006{col 61}{space 4} .0622267{col 74}{space 3} .3630866
{txt}{space 9}Nicaragua  {c |}{col 21}{res}{space 2}-.4277918{col 33}{space 2} .0860087{col 44}{space 1}   -4.97{col 53}{space 3}0.000{col 61}{space 4}-.5964817{col 74}{space 3}-.2591019
{txt}{space 8}Costa Rica  {c |}{col 21}{res}{space 2} .4578089{col 33}{space 2} .1151851{col 44}{space 1}    3.97{col 53}{space 3}0.000{col 61}{space 4} .2318951{col 74}{space 3} .6837227
{txt}{space 12}Panama  {c |}{col 21}{res}{space 2} .4867272{col 33}{space 2} .0928481{col 44}{space 1}    5.24{col 53}{space 3}0.000{col 61}{space 4} .3046232{col 74}{space 3} .6688312
{txt}{space 10}Colombia  {c |}{col 21}{res}{space 2}-.5402612{col 33}{space 2} .1021215{col 44}{space 1}   -5.29{col 53}{space 3}0.000{col 61}{space 4}-.7405533{col 74}{space 3}-.3399692
{txt}{space 11}Ecuador  {c |}{col 21}{res}{space 2}-.2673772{col 33}{space 2} .0887581{col 44}{space 1}   -3.01{col 53}{space 3}0.003{col 61}{space 4}-.4414594{col 74}{space 3} -.093295
{txt}{space 11}Bolivia  {c |}{col 21}{res}{space 2} .2823361{col 33}{space 2} .0841561{col 44}{space 1}    3.35{col 53}{space 3}0.001{col 61}{space 4} .1172797{col 74}{space 3} .4473925
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2} .1521058{col 33}{space 2} .0849553{col 44}{space 1}    1.79{col 53}{space 3}0.074{col 61}{space 4}-.0145179{col 74}{space 3} .3187295
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2}-.6694443{col 33}{space 2} .0937429{col 44}{space 1}   -7.14{col 53}{space 3}0.000{col 61}{space 4}-.8533032{col 74}{space 3}-.4855853
{txt}{space 13}Chile  {c |}{col 21}{res}{space 2} .9622189{col 33}{space 2} .0789942{col 44}{space 1}   12.18{col 53}{space 3}0.000{col 61}{space 4} .8072866{col 74}{space 3} 1.117151
{txt}{space 11}Uruguay  {c |}{col 21}{res}{space 2} 1.472481{col 33}{space 2} .0855778{col 44}{space 1}   17.21{col 53}{space 3}0.000{col 61}{space 4} 1.304636{col 74}{space 3} 1.640325
{txt}{space 12}Brazil  {c |}{col 21}{res}{space 2}-.2334409{col 33}{space 2} .1017736{col 44}{space 1}   -2.29{col 53}{space 3}0.022{col 61}{space 4}-.4330506{col 74}{space 3}-.0338313
{txt}{space 9}Venezuela  {c |}{col 21}{res}{space 2} .2096039{col 33}{space 2} .0998347{col 44}{space 1}    2.10{col 53}{space 3}0.036{col 61}{space 4}  .013797{col 74}{space 3} .4054108
{txt}{space 9}Argentina  {c |}{col 21}{res}{space 2}-.2268487{col 33}{space 2} .0991898{col 44}{space 1}   -2.29{col 53}{space 3}0.022{col 61}{space 4}-.4213908{col 74}{space 3}-.0323066
{txt}Dominican Republic  {c |}{col 21}{res}{space 2}-.0128896{col 33}{space 2} .0885794{col 44}{space 1}   -0.15{col 53}{space 3}0.884{col 61}{space 4}-.1866214{col 74}{space 3} .1608421
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2} -.095565{col 33}{space 2} .1046019{col 44}{space 1}   -0.91{col 53}{space 3}0.361{col 61}{space 4}-.3007218{col 74}{space 3} .1095918
{txt}{space 12}Guyana  {c |}{col 21}{res}{space 2}-.2100257{col 33}{space 2}  .119331{col 44}{space 1}   -1.76{col 53}{space 3}0.079{col 61}{space 4}-.4440709{col 74}{space 3} .0240195
{txt}{space 1}Trinidad & Tobago  {c |}{col 21}{res}{space 2}-.0868747{col 33}{space 2} .0875214{col 44}{space 1}   -0.99{col 53}{space 3}0.321{col 61}{space 4}-.2585315{col 74}{space 3}  .084782
{txt}{space 12}Belize  {c |}{col 21}{res}{space 2} .1560493{col 33}{space 2} .1054963{col 44}{space 1}    1.48{col 53}{space 3}0.139{col 61}{space 4}-.0508618{col 74}{space 3} .3629603
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} 2.668835{col 33}{space 2} .0779356{col 44}{space 1}   34.24{col 53}{space 3}0.000{col 61}{space 4} 2.515979{col 74}{space 3} 2.821691
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using a6.doc, dec(2) append ctitle(2010) drop(i.pais)
{txt}{stata `"shellout using `"a6.doc"'"':a6.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a6.txt""':seeout}

{com}. svy: reg trustel direct woman quintall agecohort edr rural m1 i.pais if wave==2012
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 3:27}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:6,913}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 3:334}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:6,862.5207}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:307}
{txt}{col 51}{lalign 15:F({res:11}, {res:297})}{col 66} = {res}{ralign 10:70.37}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.1147}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}            trustel{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}direct {c |}{col 21}{res}{space 2}-.1773708{col 33}{space 2} .0600088{col 44}{space 1}   -2.96{col 53}{space 3}0.003{col 61}{space 4}-.2954514{col 74}{space 3}-.0592902
{txt}{space 14}woman {c |}{col 21}{res}{space 2}-.0922208{col 33}{space 2} .0397112{col 44}{space 1}   -2.32{col 53}{space 3}0.021{col 61}{space 4}-.1703614{col 74}{space 3}-.0140803
{txt}{space 11}quintall {c |}{col 21}{res}{space 2} .0675496{col 33}{space 2} .0728413{col 44}{space 1}    0.93{col 53}{space 3}0.354{col 61}{space 4}-.0757817{col 74}{space 3} .2108809
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2} .2080316{col 33}{space 2} .0706887{col 44}{space 1}    2.94{col 53}{space 3}0.003{col 61}{space 4} .0689359{col 74}{space 3} .3471273
{txt}{space 16}edr {c |}{col 21}{res}{space 2}  .090697{col 33}{space 2} .1016617{col 44}{space 1}    0.89{col 53}{space 3}0.373{col 61}{space 4} -.109345{col 74}{space 3} .2907389
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .1260897{col 33}{space 2} .0557443{col 44}{space 1}    2.26{col 53}{space 3}0.024{col 61}{space 4} .0164005{col 74}{space 3}  .235779
{txt}{space 17}m1 {c |}{col 21}{res}{space 2} 2.374419{col 33}{space 2} .0939626{col 44}{space 1}   25.27{col 53}{space 3}0.000{col 61}{space 4} 2.189527{col 74}{space 3} 2.559311
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 10}Colombia  {c |}{col 21}{res}{space 2}-.1991199{col 33}{space 2} .0808122{col 44}{space 1}   -2.46{col 53}{space 3}0.014{col 61}{space 4}-.3581357{col 74}{space 3}-.0401041
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2} .0108141{col 33}{space 2} .0808465{col 44}{space 1}    0.13{col 53}{space 3}0.894{col 61}{space 4}-.1482693{col 74}{space 3} .1698976
{txt}{space 9}Argentina  {c |}{col 21}{res}{space 2} .4061156{col 33}{space 2} .0968894{col 44}{space 1}    4.19{col 53}{space 3}0.000{col 61}{space 4} .2154642{col 74}{space 3} .5967669
{txt}Dominican Republic  {c |}{col 21}{res}{space 2} .2993436{col 33}{space 2} .0764499{col 44}{space 1}    3.92{col 53}{space 3}0.000{col 61}{space 4} .1489114{col 74}{space 3} .4497758
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} 2.434208{col 33}{space 2} .1179402{col 44}{space 1}   20.64{col 53}{space 3}0.000{col 61}{space 4} 2.202135{col 74}{space 3} 2.666282
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using a6.doc, dec(2) append ctitle(2012) drop(i.pais)
{txt}{stata `"shellout using `"a6.doc"'"':a6.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a6.txt""':seeout}

{com}. svy: reg trustel direct woman quintall agecohort edr rural m1 i.pais if wave==2016
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 3:17}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:4,307}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 3:182}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:4,176.9786}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:165}
{txt}{col 51}{lalign 15:F({res:9}, {res:157})}{col 66} = {res}{ralign 10:97.74}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.1748}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}     trustel{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}direct {c |}{col 14}{res}{space 2}-.4058526{col 26}{space 2} .0902245{col 37}{space 1}   -4.50{col 46}{space 3}0.000{col 54}{space 4} -.583996{col 67}{space 3}-.2277092
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.0305864{col 26}{space 2} .0536244{col 37}{space 1}   -0.57{col 46}{space 3}0.569{col 54}{space 4} -.136465{col 67}{space 3} .0752922
{txt}{space 4}quintall {c |}{col 14}{res}{space 2} .1305766{col 26}{space 2} .0839937{col 37}{space 1}    1.55{col 46}{space 3}0.122{col 54}{space 4}-.0352644{col 67}{space 3} .2964176
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2}-.0661149{col 26}{space 2} .0987873{col 37}{space 1}   -0.67{col 46}{space 3}0.504{col 54}{space 4} -.261165{col 67}{space 3} .1289352
{txt}{space 9}edr {c |}{col 14}{res}{space 2} -.645148{col 26}{space 2} .1392862{col 37}{space 1}   -4.63{col 46}{space 3}0.000{col 54}{space 4} -.920161{col 67}{space 3} -.370135
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .2321275{col 26}{space 2}  .066596{col 37}{space 1}    3.49{col 46}{space 3}0.001{col 54}{space 4} .1006374{col 67}{space 3} .3636176
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 2.691162{col 26}{space 2} .1063806{col 37}{space 1}   25.30{col 46}{space 3}0.000{col 54}{space 4}  2.48112{col 67}{space 3} 2.901205
{txt}{space 12} {c |}
{space 8}pais {c |}
{space 2}Nicaragua  {c |}{col 14}{res}{space 2}-.1342718{col 26}{space 2} .0691935{col 37}{space 1}   -1.94{col 46}{space 3}0.054{col 54}{space 4}-.2708907{col 67}{space 3}  .002347
{txt}{space 3}Paraguay  {c |}{col 14}{res}{space 2}-.2028318{col 26}{space 2} .0641137{col 37}{space 1}   -3.16{col 46}{space 3}0.002{col 54}{space 4}-.3294208{col 67}{space 3}-.0762428
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 2.814588{col 26}{space 2} .1306642{col 37}{space 1}   21.54{col 46}{space 3}0.000{col 54}{space 4} 2.556598{col 67}{space 3} 3.072577
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using a6.doc, dec(2) append ctitle(2016) drop(i.pais)
{txt}{stata `"shellout using `"a6.doc"'"':a6.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a6.txt""':seeout}

{com}. svy: reg trustel direct woman quintall agecohort edr rural m1 i.pais if wave==2018
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 3:48}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:12,898}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 3:691}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:12,454.912}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:643}
{txt}{col 51}{lalign 15:F({res:15}, {res:629})}{col 66} = {res}{ralign 10:168.06}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.1462}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}            trustel{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}direct {c |}{col 21}{res}{space 2}-.2665919{col 33}{space 2} .0461791{col 44}{space 1}   -5.77{col 53}{space 3}0.000{col 61}{space 4} -.357272{col 74}{space 3}-.1759119
{txt}{space 14}woman {c |}{col 21}{res}{space 2}-.1017959{col 33}{space 2} .0324539{col 44}{space 1}   -3.14{col 53}{space 3}0.002{col 61}{space 4}-.1655244{col 74}{space 3}-.0380674
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}-.0524008{col 33}{space 2} .0469574{col 44}{space 1}   -1.12{col 53}{space 3}0.265{col 61}{space 4}-.1446093{col 74}{space 3} .0398076
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2} .2085344{col 33}{space 2} .0572477{col 44}{space 1}    3.64{col 53}{space 3}0.000{col 61}{space 4} .0961194{col 74}{space 3} .3209494
{txt}{space 16}edr {c |}{col 21}{res}{space 2} -.295391{col 33}{space 2} .0744216{col 44}{space 1}   -3.97{col 53}{space 3}0.000{col 61}{space 4}-.4415297{col 74}{space 3}-.1492523
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .2113244{col 33}{space 2} .0385236{col 44}{space 1}    5.49{col 53}{space 3}0.000{col 61}{space 4} .1356771{col 74}{space 3} .2869717
{txt}{space 17}m1 {c |}{col 21}{res}{space 2}  2.32164{col 33}{space 2} .0676302{col 44}{space 1}   34.33{col 53}{space 3}0.000{col 61}{space 4} 2.188837{col 74}{space 3} 2.454443
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2} .1780976{col 33}{space 2} .0794331{col 44}{space 1}    2.24{col 53}{space 3}0.025{col 61}{space 4}  .022118{col 74}{space 3} .3340772
{txt}{space 7}El Salvador  {c |}{col 21}{res}{space 2} .1992156{col 33}{space 2} .0705114{col 44}{space 1}    2.83{col 53}{space 3}0.005{col 61}{space 4} .0607551{col 74}{space 3} .3376761
{txt}{space 10}Honduras  {c |}{col 21}{res}{space 2}-.7423883{col 33}{space 2}  .074685{col 44}{space 1}   -9.94{col 53}{space 3}0.000{col 61}{space 4}-.8890444{col 74}{space 3}-.5957323
{txt}{space 10}Colombia  {c |}{col 21}{res}{space 2}  -.10972{col 33}{space 2} .0674581{col 44}{space 1}   -1.63{col 53}{space 3}0.104{col 61}{space 4}-.2421849{col 74}{space 3} .0227448
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2} .0349328{col 33}{space 2} .0663959{col 44}{space 1}    0.53{col 53}{space 3}0.599{col 61}{space 4}-.0954463{col 74}{space 3} .1653118
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2}-.1189475{col 33}{space 2} .0648283{col 44}{space 1}   -1.83{col 53}{space 3}0.067{col 61}{space 4}-.2462483{col 74}{space 3} .0083533
{txt}Dominican Republic  {c |}{col 21}{res}{space 2}-.4129851{col 33}{space 2} .0679645{col 44}{space 1}   -6.08{col 53}{space 3}0.000{col 61}{space 4}-.5464442{col 74}{space 3}-.2795259
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2} -.534296{col 33}{space 2} .0860225{col 44}{space 1}   -6.21{col 53}{space 3}0.000{col 61}{space 4} -.703215{col 74}{space 3}-.3653769
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} 2.510641{col 33}{space 2} .0910812{col 44}{space 1}   27.56{col 53}{space 3}0.000{col 61}{space 4} 2.331789{col 74}{space 3} 2.689494
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using a6.doc, dec(2) append ctitle(2018) drop(i.pais)
{txt}{stata `"shellout using `"a6.doc"'"':a6.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a6.txt""':seeout}

{com}.                 
. 
. ***A7: table 1 analysis for other years (indirect exposure)
. svy: reg trustel indirect woman quintall agecohort edr rural m1 i.pais i.wave
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:166}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:45,065}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:2,413}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:42,916.825}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:2,247}
{txt}{col 51}{lalign 15:F({res:30}, {res:2218})}{col 66} = {res}{ralign 10:286.87}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.1926}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}            trustel{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}indirect {c |}{col 21}{res}{space 2}-.1580395{col 33}{space 2} .0247096{col 44}{space 1}   -6.40{col 53}{space 3}0.000{col 61}{space 4}-.2064956{col 74}{space 3}-.1095835
{txt}{space 14}woman {c |}{col 21}{res}{space 2}-.0771591{col 33}{space 2}  .016605{col 44}{space 1}   -4.65{col 53}{space 3}0.000{col 61}{space 4}-.1097219{col 74}{space 3}-.0445964
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}-.0256584{col 33}{space 2}  .028074{col 44}{space 1}   -0.91{col 53}{space 3}0.361{col 61}{space 4}-.0807121{col 74}{space 3} .0293952
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2} .2499653{col 33}{space 2} .0299081{col 44}{space 1}    8.36{col 53}{space 3}0.000{col 61}{space 4}  .191315{col 74}{space 3} .3086157
{txt}{space 16}edr {c |}{col 21}{res}{space 2} .0065056{col 33}{space 2} .0422006{col 44}{space 1}    0.15{col 53}{space 3}0.877{col 61}{space 4}-.0762506{col 74}{space 3} .0892618
{txt}{space 14}rural {c |}{col 21}{res}{space 2}  .205016{col 33}{space 2} .0235187{col 44}{space 1}    8.72{col 53}{space 3}0.000{col 61}{space 4} .1588953{col 74}{space 3} .2511367
{txt}{space 17}m1 {c |}{col 21}{res}{space 2}  2.44626{col 33}{space 2} .0378761{col 44}{space 1}   64.59{col 53}{space 3}0.000{col 61}{space 4} 2.371985{col 74}{space 3} 2.520536
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2}-.0090172{col 33}{space 2} .0585094{col 44}{space 1}   -0.15{col 53}{space 3}0.878{col 61}{space 4}-.1237554{col 74}{space 3}  .105721
{txt}{space 7}El Salvador  {c |}{col 21}{res}{space 2} .2928791{col 33}{space 2} .0531341{col 44}{space 1}    5.51{col 53}{space 3}0.000{col 61}{space 4}  .188682{col 74}{space 3} .3970762
{txt}{space 10}Honduras  {c |}{col 21}{res}{space 2}-.5334303{col 33}{space 2} .0542028{col 44}{space 1}   -9.84{col 53}{space 3}0.000{col 61}{space 4}-.6397231{col 74}{space 3}-.4271375
{txt}{space 9}Nicaragua  {c |}{col 21}{res}{space 2} .1078533{col 33}{space 2} .0722085{col 44}{space 1}    1.49{col 53}{space 3}0.135{col 61}{space 4}-.0337491{col 74}{space 3} .2494558
{txt}{space 8}Costa Rica  {c |}{col 21}{res}{space 2} 1.255502{col 33}{space 2} .0812174{col 44}{space 1}   15.46{col 53}{space 3}0.000{col 61}{space 4} 1.096233{col 74}{space 3} 1.414771
{txt}{space 12}Panama  {c |}{col 21}{res}{space 2} .2530493{col 33}{space 2} .0841482{col 44}{space 1}    3.01{col 53}{space 3}0.003{col 61}{space 4} .0880329{col 74}{space 3} .4180657
{txt}{space 10}Colombia  {c |}{col 21}{res}{space 2}-.5139661{col 33}{space 2} .0723086{col 44}{space 1}   -7.11{col 53}{space 3}0.000{col 61}{space 4}-.6557647{col 74}{space 3}-.3721676
{txt}{space 11}Ecuador  {c |}{col 21}{res}{space 2}  .503871{col 33}{space 2} .0738874{col 44}{space 1}    6.82{col 53}{space 3}0.000{col 61}{space 4} .3589764{col 74}{space 3} .6487656
{txt}{space 11}Bolivia  {c |}{col 21}{res}{space 2} .0667976{col 33}{space 2} .0610337{col 44}{space 1}    1.09{col 53}{space 3}0.274{col 61}{space 4}-.0528907{col 74}{space 3}  .186486
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2} .1554508{col 33}{space 2} .0552638{col 44}{space 1}    2.81{col 53}{space 3}0.005{col 61}{space 4} .0470774{col 74}{space 3} .2638242
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2} .0199022{col 33}{space 2} .0531418{col 44}{space 1}    0.37{col 53}{space 3}0.708{col 61}{space 4}-.0843099{col 74}{space 3} .1241143
{txt}{space 13}Chile  {c |}{col 21}{res}{space 2} .9501675{col 33}{space 2} .0874231{col 44}{space 1}   10.87{col 53}{space 3}0.000{col 61}{space 4}  .778729{col 74}{space 3} 1.121606
{txt}{space 11}Uruguay  {c |}{col 21}{res}{space 2}   1.6666{col 33}{space 2} .0710581{col 44}{space 1}   23.45{col 53}{space 3}0.000{col 61}{space 4} 1.527253{col 74}{space 3} 1.805946
{txt}{space 12}Brazil  {c |}{col 21}{res}{space 2}-.5843759{col 33}{space 2} .0748122{col 44}{space 1}   -7.81{col 53}{space 3}0.000{col 61}{space 4}-.7310841{col 74}{space 3}-.4376678
{txt}{space 9}Venezuela  {c |}{col 21}{res}{space 2} .4825458{col 33}{space 2} .0879129{col 44}{space 1}    5.49{col 53}{space 3}0.000{col 61}{space 4} .3101468{col 74}{space 3} .6549449
{txt}{space 9}Argentina  {c |}{col 21}{res}{space 2} .9612923{col 33}{space 2} .0813233{col 44}{space 1}   11.82{col 53}{space 3}0.000{col 61}{space 4} .8018156{col 74}{space 3} 1.120769
{txt}Dominican Republic  {c |}{col 21}{res}{space 2}-.4488819{col 33}{space 2} .0548924{col 44}{space 1}   -8.18{col 53}{space 3}0.000{col 61}{space 4}-.5565271{col 74}{space 3}-.3412368
{txt}{space 13}Haiti  {c |}{col 21}{res}{space 2} -1.25564{col 33}{space 2} .0820203{col 44}{space 1}  -15.31{col 53}{space 3}0.000{col 61}{space 4}-1.416483{col 74}{space 3}-1.094796
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2}-.3860989{col 33}{space 2} .0578107{col 44}{space 1}   -6.68{col 53}{space 3}0.000{col 61}{space 4}-.4994669{col 74}{space 3}-.2727309
{txt}{space 12}Guyana  {c |}{col 21}{res}{space 2}-.1648359{col 33}{space 2} .0953033{col 44}{space 1}   -1.73{col 53}{space 3}0.084{col 61}{space 4}-.3517277{col 74}{space 3} .0220559
{txt}{space 12}Belize  {c |}{col 21}{res}{space 2} .1567931{col 33}{space 2} .0663728{col 44}{space 1}    2.36{col 53}{space 3}0.018{col 61}{space 4} .0266348{col 74}{space 3} .2869515
{txt}{space 19} {c |}
{space 15}wave {c |}
{space 11}2016/17  {c |}{col 21}{res}{space 2} .0661398{col 33}{space 2} .0649603{col 44}{space 1}    1.02{col 53}{space 3}0.309{col 61}{space 4}-.0612486{col 74}{space 3} .1935283
{txt}{space 11}2018/19  {c |}{col 21}{res}{space 2}-.0821985{col 33}{space 2} .0270803{col 44}{space 1}   -3.04{col 53}{space 3}0.002{col 61}{space 4}-.1353035{col 74}{space 3}-.0290935
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} 2.239549{col 33}{space 2}  .059585{col 44}{space 1}   37.59{col 53}{space 3}0.000{col 61}{space 4} 2.122701{col 74}{space 3} 2.356396
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using a7.doc, dec(2) replace ctitle(Pooled) drop(i.pais)
{txt}{stata `"shellout using `"a7.doc"'"':a7.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a7.txt""':seeout}

{com}. svy: reg trustel indirect woman quintall agecohort edr rural m1 i.pais if wave==2016
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:6}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,328}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:57}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,303.6649}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:51}
{txt}{col 51}{lalign 15:F({res:7}, {res:45})}{col 66} = {res}{ralign 10:22.43}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.1101}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}     trustel{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}indirect {c |}{col 14}{res}{space 2}-.1421175{col 26}{space 2}  .109895{col 37}{space 1}   -1.29{col 46}{space 3}0.202{col 54}{space 4}-.3627408{col 67}{space 3} .0785059
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.3760018{col 26}{space 2} .0963765{col 37}{space 1}   -3.90{col 46}{space 3}0.000{col 54}{space 4}-.5694857{col 67}{space 3} -.182518
{txt}{space 4}quintall {c |}{col 14}{res}{space 2} .1517496{col 26}{space 2} .1472584{col 37}{space 1}    1.03{col 46}{space 3}0.308{col 54}{space 4} -.143884{col 67}{space 3} .4473833
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .0734412{col 26}{space 2} .1871899{col 37}{space 1}    0.39{col 46}{space 3}0.696{col 54}{space 4}-.3023582{col 67}{space 3} .4492405
{txt}{space 9}edr {c |}{col 14}{res}{space 2}-.3763139{col 26}{space 2} .2531842{col 37}{space 1}   -1.49{col 46}{space 3}0.143{col 54}{space 4}-.8846024{col 67}{space 3} .1319746
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .3270436{col 26}{space 2} .1236287{col 37}{space 1}    2.65{col 46}{space 3}0.011{col 54}{space 4} .0788486{col 67}{space 3} .5752387
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 1.774797{col 26}{space 2}  .166987{col 37}{space 1}   10.63{col 46}{space 3}0.000{col 54}{space 4} 1.439557{col 67}{space 3} 2.110037
{txt}{space 12} {c |}
{space 8}pais {c |}
{space 3}Paraguay  {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2} 2.934173{col 26}{space 2} .2369373{col 37}{space 1}   12.38{col 46}{space 3}0.000{col 54}{space 4} 2.458501{col 67}{space 3} 3.409844
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using a7.doc, dec(2) append ctitle(2016) drop(i.pais)
{txt}{stata `"shellout using `"a7.doc"'"':a7.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a7.txt""':seeout}

{com}. svy: reg trustel indirect woman quintall agecohort edr rural m1 i.pais if wave==2018
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 3:42}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:11,230}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 3:628}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:10,942.688}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:586}
{txt}{col 51}{lalign 15:F({res:14}, {res:573})}{col 66} = {res}{ralign 10:167.62}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.1456}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}            trustel{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}indirect {c |}{col 21}{res}{space 2} -.172912{col 33}{space 2}  .039329{col 44}{space 1}   -4.40{col 53}{space 3}0.000{col 61}{space 4} -.250155{col 74}{space 3}-.0956691
{txt}{space 14}woman {c |}{col 21}{res}{space 2}-.0741825{col 33}{space 2} .0340605{col 44}{space 1}   -2.18{col 53}{space 3}0.030{col 61}{space 4}-.1410781{col 74}{space 3}-.0072869
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}-.0210002{col 33}{space 2} .0508147{col 44}{space 1}   -0.41{col 53}{space 3}0.680{col 61}{space 4}-.1208012{col 74}{space 3} .0788009
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2} .1864642{col 33}{space 2} .0617354{col 44}{space 1}    3.02{col 53}{space 3}0.003{col 61}{space 4} .0652146{col 74}{space 3} .3077138
{txt}{space 16}edr {c |}{col 21}{res}{space 2}-.3215669{col 33}{space 2} .0781662{col 44}{space 1}   -4.11{col 53}{space 3}0.000{col 61}{space 4}-.4750869{col 74}{space 3}-.1680468
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .2327954{col 33}{space 2} .0410926{col 44}{space 1}    5.67{col 53}{space 3}0.000{col 61}{space 4} .1520888{col 74}{space 3}  .313502
{txt}{space 17}m1 {c |}{col 21}{res}{space 2} 2.291599{col 33}{space 2} .0709042{col 44}{space 1}   32.32{col 53}{space 3}0.000{col 61}{space 4} 2.152342{col 74}{space 3} 2.430856
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2}  .161712{col 33}{space 2} .0811228{col 44}{space 1}    1.99{col 53}{space 3}0.047{col 61}{space 4} .0023852{col 74}{space 3} .3210387
{txt}{space 7}El Salvador  {c |}{col 21}{res}{space 2} .2011917{col 33}{space 2} .0710943{col 44}{space 1}    2.83{col 53}{space 3}0.005{col 61}{space 4}  .061561{col 74}{space 3} .3408224
{txt}{space 10}Honduras  {c |}{col 21}{res}{space 2}-.7743429{col 33}{space 2} .0757752{col 44}{space 1}  -10.22{col 53}{space 3}0.000{col 61}{space 4} -.923167{col 74}{space 3}-.6255187
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2} .0501287{col 33}{space 2} .0666226{col 44}{space 1}    0.75{col 53}{space 3}0.452{col 61}{space 4}-.0807195{col 74}{space 3} .1809768
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2}-.1004026{col 33}{space 2} .0648535{col 44}{space 1}   -1.55{col 53}{space 3}0.122{col 61}{space 4}-.2277762{col 74}{space 3}  .026971
{txt}Dominican Republic  {c |}{col 21}{res}{space 2}-.4151186{col 33}{space 2} .0680782{col 44}{space 1}   -6.10{col 53}{space 3}0.000{col 61}{space 4}-.5488256{col 74}{space 3}-.2814115
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2} -.515785{col 33}{space 2} .0874369{col 44}{space 1}   -5.90{col 53}{space 3}0.000{col 61}{space 4}-.6875129{col 74}{space 3}-.3440572
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}  2.52165{col 33}{space 2}  .094234{col 44}{space 1}   26.76{col 53}{space 3}0.000{col 61}{space 4} 2.336572{col 74}{space 3} 2.706727
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using a7.doc, dec(2) append ctitle(2018) drop(i.pais)
{txt}{stata `"shellout using `"a7.doc"'"':a7.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a7.txt""':seeout}

{com}. 
. 
. ***A8: table 1 analysis, multilevel
. mixed trustel indirect woman quintall agecohort edr rural m1 if wave==2014 || pais: 
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log likelihood = {res:-63898.751}  
Iteration 1:{space 2}Log likelihood = {res:-63898.751}  (backed up)
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects ML regression{col 53}Number of obs{col 69} = {res} 32,507
{txt}Group variable: {res}pais{col 53}{txt}Number of groups{col 69} = {res}     22
{txt}{col 53}Obs per group:
{col 66}min = {res}  1,176
{txt}{col 66}avg = {res}1,477.6
{txt}{col 66}max = {res}  2,860
{col 53}{txt}Wald chi2({res}7{txt}){col 69} = {res}4272.91
{txt}Log likelihood = {res}-63898.751{col 53}{txt}Prob > chi2{col 69} = {res} 0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     trustel{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}indirect {c |}{col 14}{res}{space 2}-.1472768{col 26}{space 2}  .029198{col 37}{space 1}   -5.04{col 46}{space 3}0.000{col 54}{space 4}-.2045038{col 67}{space 3}-.0900498
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.0700186{col 26}{space 2} .0192276{col 37}{space 1}   -3.64{col 46}{space 3}0.000{col 54}{space 4} -.107704{col 67}{space 3}-.0323332
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}-.0440775{col 26}{space 2} .0292678{col 37}{space 1}   -1.51{col 46}{space 3}0.132{col 54}{space 4}-.1014414{col 67}{space 3} .0132864
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .2584101{col 26}{space 2} .0332001{col 37}{space 1}    7.78{col 46}{space 3}0.000{col 54}{space 4} .1933392{col 67}{space 3}  .323481
{txt}{space 9}edr {c |}{col 14}{res}{space 2} .1032505{col 26}{space 2} .0461093{col 37}{space 1}    2.24{col 46}{space 3}0.025{col 54}{space 4}  .012878{col 67}{space 3} .1936229
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .1975821{col 26}{space 2}  .022394{col 37}{space 1}    8.82{col 46}{space 3}0.000{col 54}{space 4} .1536906{col 67}{space 3} .2414735
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 2.586553{col 26}{space 2} .0410259{col 37}{space 1}   63.05{col 46}{space 3}0.000{col 54}{space 4} 2.506143{col 67}{space 3} 2.666962
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.242423{col 26}{space 2}  .146108{col 37}{space 1}   15.35{col 46}{space 3}0.000{col 54}{space 4} 1.956057{col 67}{space 3} 2.528789
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}Std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}pais{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33} .4242197{col 44} .1286137{col 58} .2341672{col 70}  .768521
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 2.973954{col 44}  .023335{col 58} 2.928568{col 70} 3.020044
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}3691.46{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}
{com}.                         outreg2 using a8.doc, dec(2) replace ctitle(Indirect)
{txt}{stata `"shellout using `"a8.doc"'"':a8.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a8.txt""':seeout}

{com}. mixed trustel direct woman quintall agecohort edr rural m1 if wave==2014 || pais: 
{res}
{txt}Performing EM optimization ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log likelihood = {res: -64357.88}  
Iteration 1:{space 2}Log likelihood = {res: -64357.88}  (backed up)
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects ML regression{col 53}Number of obs{col 69} = {res} 32,745
{txt}Group variable: {res}pais{col 53}{txt}Number of groups{col 69} = {res}     22
{txt}{col 53}Obs per group:
{col 66}min = {res}  1,285
{txt}{col 66}avg = {res}1,488.4
{txt}{col 66}max = {res}  2,862
{col 53}{txt}Wald chi2({res}7{txt}){col 69} = {res}4278.20
{txt}Log likelihood = {res} -64357.88{col 53}{txt}Prob > chi2{col 69} = {res} 0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     trustel{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}direct {c |}{col 14}{res}{space 2}-.1611493{col 26}{space 2} .0354407{col 37}{space 1}   -4.55{col 46}{space 3}0.000{col 54}{space 4}-.2306119{col 67}{space 3}-.0916867
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.0687555{col 26}{space 2}  .019154{col 37}{space 1}   -3.59{col 46}{space 3}0.000{col 54}{space 4}-.1062968{col 67}{space 3}-.0312143
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}-.0388407{col 26}{space 2} .0291583{col 37}{space 1}   -1.33{col 46}{space 3}0.183{col 54}{space 4}-.0959898{col 67}{space 3} .0183085
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2}  .259893{col 26}{space 2} .0330572{col 37}{space 1}    7.86{col 46}{space 3}0.000{col 54}{space 4} .1951021{col 67}{space 3}  .324684
{txt}{space 9}edr {c |}{col 14}{res}{space 2} .0961745{col 26}{space 2} .0458818{col 37}{space 1}    2.10{col 46}{space 3}0.036{col 54}{space 4} .0062478{col 67}{space 3} .1861012
{txt}{space 7}rural {c |}{col 14}{res}{space 2}  .196731{col 26}{space 2}  .022285{col 37}{space 1}    8.83{col 46}{space 3}0.000{col 54}{space 4} .1530532{col 67}{space 3} .2404089
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 2.580671{col 26}{space 2} .0408604{col 37}{space 1}   63.16{col 46}{space 3}0.000{col 54}{space 4} 2.500586{col 67}{space 3} 2.660756
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.237943{col 26}{space 2} .1466134{col 37}{space 1}   15.26{col 46}{space 3}0.000{col 54}{space 4} 1.950586{col 67}{space 3}   2.5253
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}Std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}pais{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33} .4280439{col 44} .1297451{col 58} .2363083{col 70} .7753495
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 2.972417{col 44}  .023238{col 58} 2.927218{col 70} 3.018313
{txt}{hline 29}{c BT}{hline 48}
LR test vs. linear model:{col 27}{help j_chibar##|_new:chibar2(01) =} {res}3818.05{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}
{com}.                         outreg2 using a8.doc, dec(2) append ctitle(Direct)
{txt}{stata `"shellout using `"a8.doc"'"':a8.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a8.txt""':seeout}

{com}.         
.         
. ***A9: table 1 analysis controlling for support for democracy
. *unmatched
. svy: reg trustel indirect woman quintall agecohort edr rural m1 ing4 i.pais if wave==2014
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:118}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:31,416}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:1,726}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:29,631.834}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,608}
{txt}{col 51}{lalign 15:F({res:29}, {res:1580})}{col 66} = {res}{ralign 10:234.00}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.2263}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}            trustel{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}indirect {c |}{col 21}{res}{space 2} -.143569{col 33}{space 2} .0329018{col 44}{space 1}   -4.36{col 53}{space 3}0.000{col 61}{space 4}-.2081039{col 74}{space 3} -.079034
{txt}{space 14}woman {c |}{col 21}{res}{space 2} -.053231{col 33}{space 2} .0193839{col 44}{space 1}   -2.75{col 53}{space 3}0.006{col 61}{space 4}-.0912515{col 74}{space 3}-.0152106
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}-.0576535{col 33}{space 2} .0342078{col 44}{space 1}   -1.69{col 53}{space 3}0.092{col 61}{space 4}-.1247501{col 74}{space 3} .0094431
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2} .1963497{col 33}{space 2} .0342749{col 44}{space 1}    5.73{col 53}{space 3}0.000{col 61}{space 4} .1291215{col 74}{space 3} .2635779
{txt}{space 16}edr {c |}{col 21}{res}{space 2} .0519191{col 33}{space 2} .0507801{col 44}{space 1}    1.02{col 53}{space 3}0.307{col 61}{space 4}-.0476831{col 74}{space 3} .1515214
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .1852057{col 33}{space 2} .0289093{col 44}{space 1}    6.41{col 53}{space 3}0.000{col 61}{space 4}  .128502{col 74}{space 3} .2419095
{txt}{space 17}m1 {c |}{col 21}{res}{space 2} 2.492981{col 33}{space 2} .0490475{col 44}{space 1}   50.83{col 53}{space 3}0.000{col 61}{space 4} 2.396777{col 74}{space 3} 2.589184
{txt}{space 15}ing4 {c |}{col 21}{res}{space 2} .9197719{col 33}{space 2} .0421086{col 44}{space 1}   21.84{col 53}{space 3}0.000{col 61}{space 4} .8371785{col 74}{space 3} 1.002365
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2}-.2371011{col 33}{space 2} .0920699{col 44}{space 1}   -2.58{col 53}{space 3}0.010{col 61}{space 4}-.4176907{col 74}{space 3}-.0565115
{txt}{space 7}El Salvador  {c |}{col 21}{res}{space 2} .3292097{col 33}{space 2} .0789444{col 44}{space 1}    4.17{col 53}{space 3}0.000{col 61}{space 4} .1743649{col 74}{space 3} .4840545
{txt}{space 10}Honduras  {c |}{col 21}{res}{space 2}-.3835564{col 33}{space 2}  .081051{col 44}{space 1}   -4.73{col 53}{space 3}0.000{col 61}{space 4} -.542533{col 74}{space 3}-.2245798
{txt}{space 9}Nicaragua  {c |}{col 21}{res}{space 2} .1086412{col 33}{space 2} .0846254{col 44}{space 1}    1.28{col 53}{space 3}0.199{col 61}{space 4}-.0573465{col 74}{space 3} .2746289
{txt}{space 8}Costa Rica  {c |}{col 21}{res}{space 2} 1.209133{col 33}{space 2} .0895434{col 44}{space 1}   13.50{col 53}{space 3}0.000{col 61}{space 4} 1.033499{col 74}{space 3} 1.384767
{txt}{space 12}Panama  {c |}{col 21}{res}{space 2} .3486769{col 33}{space 2} .0897959{col 44}{space 1}    3.88{col 53}{space 3}0.000{col 61}{space 4} .1725477{col 74}{space 3} .5248062
{txt}{space 10}Colombia  {c |}{col 21}{res}{space 2}-.5183347{col 33}{space 2} .0826353{col 44}{space 1}   -6.27{col 53}{space 3}0.000{col 61}{space 4} -.680419{col 74}{space 3}-.3562504
{txt}{space 11}Ecuador  {c |}{col 21}{res}{space 2} .4771058{col 33}{space 2} .0854519{col 44}{space 1}    5.58{col 53}{space 3}0.000{col 61}{space 4} .3094969{col 74}{space 3} .6447147
{txt}{space 11}Bolivia  {c |}{col 21}{res}{space 2} .0767055{col 33}{space 2} .0762231{col 44}{space 1}    1.01{col 53}{space 3}0.314{col 61}{space 4}-.0728016{col 74}{space 3} .2262126
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2} .3166679{col 33}{space 2} .0875631{col 44}{space 1}    3.62{col 53}{space 3}0.000{col 61}{space 4} .1449181{col 74}{space 3} .4884177
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2}  .140652{col 33}{space 2} .0823852{col 44}{space 1}    1.71{col 53}{space 3}0.088{col 61}{space 4}-.0209417{col 74}{space 3} .3022457
{txt}{space 13}Chile  {c |}{col 21}{res}{space 2} .8965817{col 33}{space 2}  .093909{col 44}{space 1}    9.55{col 53}{space 3}0.000{col 61}{space 4} .7123849{col 74}{space 3} 1.080779
{txt}{space 11}Uruguay  {c |}{col 21}{res}{space 2} 1.514589{col 33}{space 2} .0837612{col 44}{space 1}   18.08{col 53}{space 3}0.000{col 61}{space 4} 1.350296{col 74}{space 3} 1.678882
{txt}{space 12}Brazil  {c |}{col 21}{res}{space 2}-.5495476{col 33}{space 2} .0856825{col 44}{space 1}   -6.41{col 53}{space 3}0.000{col 61}{space 4}-.7176088{col 74}{space 3}-.3814865
{txt}{space 9}Venezuela  {c |}{col 21}{res}{space 2} .4153651{col 33}{space 2} .0972319{col 44}{space 1}    4.27{col 53}{space 3}0.000{col 61}{space 4} .2246505{col 74}{space 3} .6060796
{txt}{space 9}Argentina  {c |}{col 21}{res}{space 2} .8396561{col 33}{space 2} .0891677{col 44}{space 1}    9.42{col 53}{space 3}0.000{col 61}{space 4}  .664759{col 74}{space 3} 1.014553
{txt}Dominican Republic  {c |}{col 21}{res}{space 2}-.5924074{col 33}{space 2} .0868937{col 44}{space 1}   -6.82{col 53}{space 3}0.000{col 61}{space 4}-.7628443{col 74}{space 3}-.4219705
{txt}{space 13}Haiti  {c |}{col 21}{res}{space 2}-1.213684{col 33}{space 2} .0916656{col 44}{space 1}  -13.24{col 53}{space 3}0.000{col 61}{space 4} -1.39348{col 74}{space 3}-1.033887
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2}-.2158935{col 33}{space 2} .0760129{col 44}{space 1}   -2.84{col 53}{space 3}0.005{col 61}{space 4}-.3649882{col 74}{space 3}-.0667987
{txt}{space 12}Guyana  {c |}{col 21}{res}{space 2}-.1625009{col 33}{space 2} .1058089{col 44}{space 1}   -1.54{col 53}{space 3}0.125{col 61}{space 4}-.3700387{col 74}{space 3}  .045037
{txt}{space 12}Belize  {c |}{col 21}{res}{space 2} .1246925{col 33}{space 2} .0782246{col 44}{space 1}    1.59{col 53}{space 3}0.111{col 61}{space 4}-.0287404{col 74}{space 3} .2781253
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} 1.585647{col 33}{space 2} .0791306{col 44}{space 1}   20.04{col 53}{space 3}0.000{col 61}{space 4} 1.430437{col 74}{space 3} 1.740856
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using a9.doc, dec(2) replace ctitle(Indirect, unmatched) drop(i.pais)
{txt}{stata `"shellout using `"a9.doc"'"':a9.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a9.txt""':seeout}

{com}. svy: reg trustel direct woman quintall agecohort edr rural m1 ing4 i.pais if wave==2014
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:118}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:31,638}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:1,726}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:29,848.106}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,608}
{txt}{col 51}{lalign 15:F({res:29}, {res:1580})}{col 66} = {res}{ralign 10:234.00}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.2266}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}            trustel{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}direct {c |}{col 21}{res}{space 2}-.1450773{col 33}{space 2} .0389921{col 44}{space 1}   -3.72{col 53}{space 3}0.000{col 61}{space 4} -.221558{col 74}{space 3}-.0685966
{txt}{space 14}woman {c |}{col 21}{res}{space 2}-.0515371{col 33}{space 2} .0191958{col 44}{space 1}   -2.68{col 53}{space 3}0.007{col 61}{space 4}-.0891885{col 74}{space 3}-.0138856
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}-.0513175{col 33}{space 2} .0342295{col 44}{space 1}   -1.50{col 53}{space 3}0.134{col 61}{space 4}-.1184566{col 74}{space 3} .0158215
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2} .1990436{col 33}{space 2} .0342916{col 44}{space 1}    5.80{col 53}{space 3}0.000{col 61}{space 4} .1317827{col 74}{space 3} .2663045
{txt}{space 16}edr {c |}{col 21}{res}{space 2} .0424824{col 33}{space 2} .0505717{col 44}{space 1}    0.84{col 53}{space 3}0.401{col 61}{space 4} -.056711{col 74}{space 3} .1416757
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .1825841{col 33}{space 2} .0288302{col 44}{space 1}    6.33{col 53}{space 3}0.000{col 61}{space 4} .1260355{col 74}{space 3} .2391328
{txt}{space 17}m1 {c |}{col 21}{res}{space 2} 2.491779{col 33}{space 2} .0490355{col 44}{space 1}   50.82{col 53}{space 3}0.000{col 61}{space 4} 2.395599{col 74}{space 3} 2.587959
{txt}{space 15}ing4 {c |}{col 21}{res}{space 2} .9126876{col 33}{space 2} .0419386{col 44}{space 1}   21.76{col 53}{space 3}0.000{col 61}{space 4} .8304276{col 74}{space 3} .9949477
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2}-.2288705{col 33}{space 2} .0928963{col 44}{space 1}   -2.46{col 53}{space 3}0.014{col 61}{space 4} -.411081{col 74}{space 3}  -.04666
{txt}{space 7}El Salvador  {c |}{col 21}{res}{space 2}  .326729{col 33}{space 2} .0783657{col 44}{space 1}    4.17{col 53}{space 3}0.000{col 61}{space 4} .1730193{col 74}{space 3} .4804386
{txt}{space 10}Honduras  {c |}{col 21}{res}{space 2} -.383912{col 33}{space 2} .0803947{col 44}{space 1}   -4.78{col 53}{space 3}0.000{col 61}{space 4}-.5416014{col 74}{space 3}-.2262227
{txt}{space 9}Nicaragua  {c |}{col 21}{res}{space 2} .1130273{col 33}{space 2} .0839999{col 44}{space 1}    1.35{col 53}{space 3}0.179{col 61}{space 4}-.0517335{col 74}{space 3} .2777882
{txt}{space 8}Costa Rica  {c |}{col 21}{res}{space 2} 1.222345{col 33}{space 2} .0876014{col 44}{space 1}   13.95{col 53}{space 3}0.000{col 61}{space 4}  1.05052{col 74}{space 3}  1.39417
{txt}{space 12}Panama  {c |}{col 21}{res}{space 2} .3532614{col 33}{space 2} .0890466{col 44}{space 1}    3.97{col 53}{space 3}0.000{col 61}{space 4} .1786017{col 74}{space 3}  .527921
{txt}{space 10}Colombia  {c |}{col 21}{res}{space 2}-.5253346{col 33}{space 2}  .082316{col 44}{space 1}   -6.38{col 53}{space 3}0.000{col 61}{space 4}-.6867926{col 74}{space 3}-.3638767
{txt}{space 11}Ecuador  {c |}{col 21}{res}{space 2} .4813371{col 33}{space 2} .0851708{col 44}{space 1}    5.65{col 53}{space 3}0.000{col 61}{space 4} .3142797{col 74}{space 3} .6483945
{txt}{space 11}Bolivia  {c |}{col 21}{res}{space 2} .0746297{col 33}{space 2} .0760757{col 44}{space 1}    0.98{col 53}{space 3}0.327{col 61}{space 4}-.0745882{col 74}{space 3} .2238475
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2} .3186688{col 33}{space 2} .0874235{col 44}{space 1}    3.65{col 53}{space 3}0.000{col 61}{space 4} .1471928{col 74}{space 3} .4901448
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2} .1417612{col 33}{space 2} .0815398{col 44}{space 1}    1.74{col 53}{space 3}0.082{col 61}{space 4}-.0181743{col 74}{space 3} .3016966
{txt}{space 13}Chile  {c |}{col 21}{res}{space 2} .9005347{col 33}{space 2} .0929908{col 44}{space 1}    9.68{col 53}{space 3}0.000{col 61}{space 4} .7181389{col 74}{space 3} 1.082931
{txt}{space 11}Uruguay  {c |}{col 21}{res}{space 2} 1.512387{col 33}{space 2} .0832992{col 44}{space 1}   18.16{col 53}{space 3}0.000{col 61}{space 4} 1.349001{col 74}{space 3} 1.675774
{txt}{space 12}Brazil  {c |}{col 21}{res}{space 2}-.5588034{col 33}{space 2} .0855094{col 44}{space 1}   -6.53{col 53}{space 3}0.000{col 61}{space 4}-.7265249{col 74}{space 3}-.3910819
{txt}{space 9}Venezuela  {c |}{col 21}{res}{space 2} .4251646{col 33}{space 2} .0967393{col 44}{space 1}    4.39{col 53}{space 3}0.000{col 61}{space 4} .2354163{col 74}{space 3}  .614913
{txt}{space 9}Argentina  {c |}{col 21}{res}{space 2} .8323301{col 33}{space 2} .0891293{col 44}{space 1}    9.34{col 53}{space 3}0.000{col 61}{space 4} .6575083{col 74}{space 3} 1.007152
{txt}Dominican Republic  {c |}{col 21}{res}{space 2}-.6068272{col 33}{space 2} .0872744{col 44}{space 1}   -6.95{col 53}{space 3}0.000{col 61}{space 4}-.7780107{col 74}{space 3}-.4356438
{txt}{space 13}Haiti  {c |}{col 21}{res}{space 2}-1.241755{col 33}{space 2}  .091639{col 44}{space 1}  -13.55{col 53}{space 3}0.000{col 61}{space 4}-1.421499{col 74}{space 3}-1.062011
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2}-.2305863{col 33}{space 2} .0766379{col 44}{space 1}   -3.01{col 53}{space 3}0.003{col 61}{space 4}-.3809069{col 74}{space 3}-.0802657
{txt}{space 12}Guyana  {c |}{col 21}{res}{space 2}-.1619485{col 33}{space 2} .1057099{col 44}{space 1}   -1.53{col 53}{space 3}0.126{col 61}{space 4}-.3692922{col 74}{space 3} .0453953
{txt}{space 12}Belize  {c |}{col 21}{res}{space 2} .1138101{col 33}{space 2} .0775482{col 44}{space 1}    1.47{col 53}{space 3}0.142{col 61}{space 4}-.0382962{col 74}{space 3} .2659163
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} 1.584946{col 33}{space 2} .0784747{col 44}{space 1}   20.20{col 53}{space 3}0.000{col 61}{space 4} 1.431022{col 74}{space 3} 1.738869
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using a9.doc, dec(2) append ctitle(Direct, unmatched) drop(i.pais)
{txt}{stata `"shellout using `"a9.doc"'"':a9.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a9.txt""':seeout}

{com}. *matched
. reg trustel indirect woman quintall agecohort edr rural m1 ing4 i.pais if wave==2014 [iweight=indirectwt]

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     8,330
{txt}{hline 13}{c +}{hline 34}   F(29, 8300)     = {res}    64.90
{txt}       Model {c |} {res} 5786.81621        29  199.545387   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 25519.5194     8,300  3.07464089   {txt}R-squared       ={res}    0.1848
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1820
{txt}       Total {c |} {res} 31306.3356     8,329   3.7587148   {txt}Root MSE        =   {res} 1.7534

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            trustel{col 21}{c |} Coefficient{col 33}  Std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}indirect {c |}{col 21}{res}{space 2} -.148025{col 33}{space 2} .0384896{col 44}{space 1}   -3.85{col 53}{space 3}0.000{col 61}{space 4}-.2234742{col 74}{space 3}-.0725759
{txt}{space 14}woman {c |}{col 21}{res}{space 2}-.0678881{col 33}{space 2} .0386636{col 44}{space 1}   -1.76{col 53}{space 3}0.079{col 61}{space 4}-.1436784{col 74}{space 3} .0079022
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}-.1121184{col 33}{space 2} .0585405{col 44}{space 1}   -1.92{col 53}{space 3}0.055{col 61}{space 4}-.2268724{col 74}{space 3} .0026355
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2} .1641256{col 33}{space 2}  .072261{col 44}{space 1}    2.27{col 53}{space 3}0.023{col 61}{space 4}  .022476{col 74}{space 3} .3057751
{txt}{space 16}edr {c |}{col 21}{res}{space 2}-.0201208{col 33}{space 2} .0928688{col 44}{space 1}   -0.22{col 53}{space 3}0.828{col 61}{space 4}-.2021668{col 74}{space 3} .1619252
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .2141588{col 33}{space 2}  .043969{col 44}{space 1}    4.87{col 53}{space 3}0.000{col 61}{space 4} .1279686{col 74}{space 3}  .300349
{txt}{space 17}m1 {c |}{col 21}{res}{space 2}  2.34067{col 33}{space 2}  .081449{col 44}{space 1}   28.74{col 53}{space 3}0.000{col 61}{space 4}  2.18101{col 74}{space 3} 2.500331
{txt}{space 15}ing4 {c |}{col 21}{res}{space 2} .7250857{col 33}{space 2} .0674381{col 44}{space 1}   10.75{col 53}{space 3}0.000{col 61}{space 4} .5928902{col 74}{space 3} .8572813
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2}-.0104973{col 33}{space 2} .1165598{col 44}{space 1}   -0.09{col 53}{space 3}0.928{col 61}{space 4}-.2389836{col 74}{space 3} .2179889
{txt}{space 7}El Salvador  {c |}{col 21}{res}{space 2} .4875582{col 33}{space 2} .1213918{col 44}{space 1}    4.02{col 53}{space 3}0.000{col 61}{space 4}    .2496{col 74}{space 3} .7255164
{txt}{space 10}Honduras  {c |}{col 21}{res}{space 2}-.3344925{col 33}{space 2} .1018963{col 44}{space 1}   -3.28{col 53}{space 3}0.001{col 61}{space 4}-.5342348{col 74}{space 3}-.1347502
{txt}{space 9}Nicaragua  {c |}{col 21}{res}{space 2} .2386546{col 33}{space 2} .1302837{col 44}{space 1}    1.83{col 53}{space 3}0.067{col 61}{space 4}-.0167341{col 74}{space 3} .4940433
{txt}{space 8}Costa Rica  {c |}{col 21}{res}{space 2}  .966445{col 33}{space 2} .2198947{col 44}{space 1}    4.40{col 53}{space 3}0.000{col 61}{space 4} .5353964{col 74}{space 3} 1.397494
{txt}{space 12}Panama  {c |}{col 21}{res}{space 2} .4870563{col 33}{space 2} .1363241{col 44}{space 1}    3.57{col 53}{space 3}0.000{col 61}{space 4} .2198269{col 74}{space 3} .7542857
{txt}{space 10}Colombia  {c |}{col 21}{res}{space 2}-.3644938{col 33}{space 2} .1125762{col 44}{space 1}   -3.24{col 53}{space 3}0.001{col 61}{space 4}-.5851714{col 74}{space 3}-.1438163
{txt}{space 11}Ecuador  {c |}{col 21}{res}{space 2} .6240588{col 33}{space 2}  .130668{col 44}{space 1}    4.78{col 53}{space 3}0.000{col 61}{space 4} .3679169{col 74}{space 3} .8802006
{txt}{space 11}Bolivia  {c |}{col 21}{res}{space 2}  .199759{col 33}{space 2} .1224031{col 44}{space 1}    1.63{col 53}{space 3}0.103{col 61}{space 4}-.0401817{col 74}{space 3} .4396996
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2} .3973517{col 33}{space 2} .1332865{col 44}{space 1}    2.98{col 53}{space 3}0.003{col 61}{space 4} .1360768{col 74}{space 3} .6586265
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2} .3213309{col 33}{space 2} .1040051{col 44}{space 1}    3.09{col 53}{space 3}0.002{col 61}{space 4} .1174549{col 74}{space 3} .5252068
{txt}{space 13}Chile  {c |}{col 21}{res}{space 2} 1.330123{col 33}{space 2} .2166462{col 44}{space 1}    6.14{col 53}{space 3}0.000{col 61}{space 4} .9054427{col 74}{space 3} 1.754804
{txt}{space 11}Uruguay  {c |}{col 21}{res}{space 2} 1.610171{col 33}{space 2} .1267581{col 44}{space 1}   12.70{col 53}{space 3}0.000{col 61}{space 4} 1.361693{col 74}{space 3} 1.858648
{txt}{space 12}Brazil  {c |}{col 21}{res}{space 2}-.3568724{col 33}{space 2} .0999573{col 44}{space 1}   -3.57{col 53}{space 3}0.000{col 61}{space 4}-.5528137{col 74}{space 3}-.1609311
{txt}{space 9}Venezuela  {c |}{col 21}{res}{space 2} .5796311{col 33}{space 2} .1817215{col 44}{space 1}    3.19{col 53}{space 3}0.001{col 61}{space 4} .2234116{col 74}{space 3} .9358506
{txt}{space 9}Argentina  {c |}{col 21}{res}{space 2} .9555787{col 33}{space 2} .1535283{col 44}{space 1}    6.22{col 53}{space 3}0.000{col 61}{space 4}  .654625{col 74}{space 3} 1.256532
{txt}Dominican Republic  {c |}{col 21}{res}{space 2}-.4759937{col 33}{space 2} .0953956{col 44}{space 1}   -4.99{col 53}{space 3}0.000{col 61}{space 4} -.662993{col 74}{space 3}-.2889944
{txt}{space 13}Haiti  {c |}{col 21}{res}{space 2}-.8927008{col 33}{space 2} .1078499{col 44}{space 1}   -8.28{col 53}{space 3}0.000{col 61}{space 4}-1.104114{col 74}{space 3}-.6812881
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2}-.1547684{col 33}{space 2} .1167942{col 44}{space 1}   -1.33{col 53}{space 3}0.185{col 61}{space 4}-.3837142{col 74}{space 3} .0741774
{txt}{space 12}Guyana  {c |}{col 21}{res}{space 2} .0613116{col 33}{space 2} .1624054{col 44}{space 1}    0.38{col 53}{space 3}0.706{col 61}{space 4}-.2570436{col 74}{space 3} .3796667
{txt}{space 12}Belize  {c |}{col 21}{res}{space 2} .2403057{col 33}{space 2} .0951536{col 44}{space 1}    2.53{col 53}{space 3}0.012{col 61}{space 4} .0537808{col 74}{space 3} .4268306
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} 1.760668{col 33}{space 2} .1151362{col 44}{space 1}   15.29{col 53}{space 3}0.000{col 61}{space 4} 1.534972{col 74}{space 3} 1.986363
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using a9.doc, dec(2) append ctitle(Indirect, matched) drop(i.pais)
{txt}{stata `"shellout using `"a9.doc"'"':a9.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a9.txt""':seeout}

{com}. reg trustel direct woman quintall agecohort edr rural m1 ing4 i.pais if wave==2014 [iweight=directwt]

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     5,240
{txt}{hline 13}{c +}{hline 34}   F(29, 5210)     = {res}    36.45
{txt}       Model {c |} {res} 3294.36429        29  113.598769   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 16236.8667     5,210  3.11648114   {txt}R-squared       ={res}    0.1687
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1641
{txt}       Total {c |} {res}  19531.231     5,239  3.72804563   {txt}Root MSE        =   {res} 1.7653

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            trustel{col 21}{c |} Coefficient{col 33}  Std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}direct {c |}{col 21}{res}{space 2}-.1604711{col 33}{space 2} .0489095{col 44}{space 1}   -3.28{col 53}{space 3}0.001{col 61}{space 4}-.2563543{col 74}{space 3}-.0645879
{txt}{space 14}woman {c |}{col 21}{res}{space 2}-.0342944{col 33}{space 2} .0493023{col 44}{space 1}   -0.70{col 53}{space 3}0.487{col 61}{space 4}-.1309477{col 74}{space 3} .0623588
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}-.1422288{col 33}{space 2} .0729291{col 44}{space 1}   -1.95{col 53}{space 3}0.051{col 61}{space 4}-.2852004{col 74}{space 3} .0007427
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2} .1789781{col 33}{space 2} .0926975{col 44}{space 1}    1.93{col 53}{space 3}0.054{col 61}{space 4}-.0027479{col 74}{space 3} .3607041
{txt}{space 16}edr {c |}{col 21}{res}{space 2}-.0622898{col 33}{space 2} .1160261{col 44}{space 1}   -0.54{col 53}{space 3}0.591{col 61}{space 4}-.2897495{col 74}{space 3}   .16517
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .2660656{col 33}{space 2} .0549249{col 44}{space 1}    4.84{col 53}{space 3}0.000{col 61}{space 4} .1583897{col 74}{space 3} .3737415
{txt}{space 17}m1 {c |}{col 21}{res}{space 2} 2.360317{col 33}{space 2} .1040752{col 44}{space 1}   22.68{col 53}{space 3}0.000{col 61}{space 4} 2.156285{col 74}{space 3} 2.564348
{txt}{space 15}ing4 {c |}{col 21}{res}{space 2} .6140106{col 33}{space 2} .0859553{col 44}{space 1}    7.14{col 53}{space 3}0.000{col 61}{space 4} .4455022{col 74}{space 3} .7825191
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2} .0633686{col 33}{space 2} .1329019{col 44}{space 1}    0.48{col 53}{space 3}0.634{col 61}{space 4}-.1971748{col 74}{space 3}  .323912
{txt}{space 7}El Salvador  {c |}{col 21}{res}{space 2}  .380437{col 33}{space 2} .1849818{col 44}{space 1}    2.06{col 53}{space 3}0.040{col 61}{space 4}  .017795{col 74}{space 3}  .743079
{txt}{space 10}Honduras  {c |}{col 21}{res}{space 2} -.376412{col 33}{space 2} .1202683{col 44}{space 1}   -3.13{col 53}{space 3}0.002{col 61}{space 4}-.6121883{col 74}{space 3}-.1406358
{txt}{space 9}Nicaragua  {c |}{col 21}{res}{space 2}-.0452954{col 33}{space 2} .1648255{col 44}{space 1}   -0.27{col 53}{space 3}0.783{col 61}{space 4}-.3684226{col 74}{space 3} .2778317
{txt}{space 8}Costa Rica  {c |}{col 21}{res}{space 2} .7962527{col 33}{space 2}  .241269{col 44}{space 1}    3.30{col 53}{space 3}0.001{col 61}{space 4} .3232642{col 74}{space 3} 1.269241
{txt}{space 12}Panama  {c |}{col 21}{res}{space 2} .4659042{col 33}{space 2} .1641954{col 44}{space 1}    2.84{col 53}{space 3}0.005{col 61}{space 4} .1440124{col 74}{space 3}  .787796
{txt}{space 10}Colombia  {c |}{col 21}{res}{space 2}-.3344814{col 33}{space 2} .1491945{col 44}{space 1}   -2.24{col 53}{space 3}0.025{col 61}{space 4}-.6269651{col 74}{space 3}-.0419977
{txt}{space 11}Ecuador  {c |}{col 21}{res}{space 2} .6565555{col 33}{space 2} .1471585{col 44}{space 1}    4.46{col 53}{space 3}0.000{col 61}{space 4} .3680632{col 74}{space 3} .9450478
{txt}{space 11}Bolivia  {c |}{col 21}{res}{space 2} .1088332{col 33}{space 2} .1359758{col 44}{space 1}    0.80{col 53}{space 3}0.424{col 61}{space 4}-.1577364{col 74}{space 3} .3754029
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2} .3293256{col 33}{space 2} .1674031{col 44}{space 1}    1.97{col 53}{space 3}0.049{col 61}{space 4} .0011452{col 74}{space 3} .6575059
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2} .2502452{col 33}{space 2} .1339205{col 44}{space 1}    1.87{col 53}{space 3}0.062{col 61}{space 4}-.0122951{col 74}{space 3} .5127856
{txt}{space 13}Chile  {c |}{col 21}{res}{space 2} 1.037007{col 33}{space 2} .2802756{col 44}{space 1}    3.70{col 53}{space 3}0.000{col 61}{space 4} .4875495{col 74}{space 3} 1.586465
{txt}{space 11}Uruguay  {c |}{col 21}{res}{space 2} 1.619179{col 33}{space 2} .1997783{col 44}{space 1}    8.10{col 53}{space 3}0.000{col 61}{space 4}  1.22753{col 74}{space 3} 2.010828
{txt}{space 12}Brazil  {c |}{col 21}{res}{space 2}-.4966393{col 33}{space 2} .1343491{col 44}{space 1}   -3.70{col 53}{space 3}0.000{col 61}{space 4}-.7600199{col 74}{space 3}-.2332586
{txt}{space 9}Venezuela  {c |}{col 21}{res}{space 2} .6043597{col 33}{space 2} .2178937{col 44}{space 1}    2.77{col 53}{space 3}0.006{col 61}{space 4} .1771967{col 74}{space 3} 1.031523
{txt}{space 9}Argentina  {c |}{col 21}{res}{space 2} .7235228{col 33}{space 2} .2381235{col 44}{space 1}    3.04{col 53}{space 3}0.002{col 61}{space 4} .2567009{col 74}{space 3} 1.190345
{txt}Dominican Republic  {c |}{col 21}{res}{space 2} -.616349{col 33}{space 2}  .115298{col 44}{space 1}   -5.35{col 53}{space 3}0.000{col 61}{space 4}-.8423814{col 74}{space 3}-.3903166
{txt}{space 13}Haiti  {c |}{col 21}{res}{space 2}-.9156241{col 33}{space 2} .1471856{col 44}{space 1}   -6.22{col 53}{space 3}0.000{col 61}{space 4} -1.20417{col 74}{space 3}-.6270786
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2} -.266602{col 33}{space 2} .1656586{col 44}{space 1}   -1.61{col 53}{space 3}0.108{col 61}{space 4}-.5913623{col 74}{space 3} .0581584
{txt}{space 12}Guyana  {c |}{col 21}{res}{space 2}-.0998619{col 33}{space 2} .2501918{col 44}{space 1}   -0.40{col 53}{space 3}0.690{col 61}{space 4}-.5903427{col 74}{space 3} .3906188
{txt}{space 12}Belize  {c |}{col 21}{res}{space 2} .1404664{col 33}{space 2} .1213084{col 44}{space 1}    1.16{col 53}{space 3}0.247{col 61}{space 4}-.0973489{col 74}{space 3} .3782818
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} 1.889189{col 33}{space 2} .1443461{col 44}{space 1}   13.09{col 53}{space 3}0.000{col 61}{space 4}  1.60621{col 74}{space 3} 2.172168
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using a9.doc, dec(2) append ctitle(Direct, matched) drop(i.pais)
{txt}{stata `"shellout using `"a9.doc"'"':a9.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a9.txt""':seeout}

{com}.         
.         
. ***A10: country-by-country analysis for H1, direct
. svy: reg trustel direct woman quintall agecohort edr rural m1 if wave==2014 & pais==1
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 3:4}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,435}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 3:130}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,402.2802}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:126}
{txt}{col 51}{lalign 15:F({res:7}, {res:120})}{col 66} = {res}{ralign 10:49.18}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.2210}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}     trustel{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}direct {c |}{col 14}{res}{space 2}-.1688264{col 26}{space 2} .1295465{col 37}{space 1}   -1.30{col 46}{space 3}0.195{col 54}{space 4}-.4251951{col 67}{space 3} .0875422
{txt}{space 7}woman {c |}{col 14}{res}{space 2} .0194624{col 26}{space 2} .0933981{col 37}{space 1}    0.21{col 46}{space 3}0.835{col 54}{space 4}-.1653698{col 67}{space 3} .2042945
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}-.1699336{col 26}{space 2} .1467296{col 37}{space 1}   -1.16{col 46}{space 3}0.249{col 54}{space 4}-.4603071{col 67}{space 3} .1204399
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .2720637{col 26}{space 2} .1440351{col 37}{space 1}    1.89{col 46}{space 3}0.061{col 54}{space 4}-.0129776{col 67}{space 3} .5571051
{txt}{space 9}edr {c |}{col 14}{res}{space 2}-.1523244{col 26}{space 2} .2344378{col 37}{space 1}   -0.65{col 46}{space 3}0.517{col 54}{space 4}-.6162699{col 67}{space 3} .3116212
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .3398699{col 26}{space 2} .1863539{col 37}{space 1}    1.82{col 46}{space 3}0.071{col 54}{space 4}-.0289189{col 67}{space 3} .7086587
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 3.017977{col 26}{space 2} .1929079{col 37}{space 1}   15.64{col 46}{space 3}0.000{col 54}{space 4} 2.636218{col 67}{space 3} 3.399736
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.037572{col 26}{space 2} .2219363{col 37}{space 1}    9.18{col 46}{space 3}0.000{col 54}{space 4} 1.598367{col 67}{space 3} 2.476778
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using a10.doc, dec(2) replace ctitle(Mexico)
{txt}{stata `"shellout using `"a10.doc"'"':a10.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a10.txt""':seeout}

{com}. levelsof pais if wave==2014 & !missing(trustel, indirect) & pais>1, local(2014direct)
{res}{txt}2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 21 22 23 24 26

{com}. foreach x in `2014direct' {c -(}
{txt}  2{com}.                 svy: reg b47a direct woman quintall agecohort edr rural m1 if wave==2014 & pais==`x'
{txt}  3{com}.                 outreg2 using a10.doc, dec(2) append ctitle(`x')
{txt}  4{com}. {c )-}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:4}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,414}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:62}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,408.3666}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:58}
{txt}{col 51}{lalign 15:F({res:7}, {res:52})}{col 66} = {res}{ralign 10:12.66}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0769}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}direct {c |}{col 14}{res}{space 2} .4866139{col 26}{space 2} .1729065{col 37}{space 1}    2.81{col 46}{space 3}0.007{col 54}{space 4}  .140504{col 67}{space 3} .8327238
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.0536002{col 26}{space 2} .0816864{col 37}{space 1}   -0.66{col 46}{space 3}0.514{col 54}{space 4}-.2171133{col 67}{space 3} .1099129
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}-.2750537{col 26}{space 2} .1652296{col 37}{space 1}   -1.66{col 46}{space 3}0.101{col 54}{space 4}-.6057968{col 67}{space 3} .0556894
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .2625192{col 26}{space 2} .1474169{col 37}{space 1}    1.78{col 46}{space 3}0.080{col 54}{space 4}-.0325679{col 67}{space 3} .5576062
{txt}{space 9}edr {c |}{col 14}{res}{space 2} .1052358{col 26}{space 2} .2200054{col 37}{space 1}    0.48{col 46}{space 3}0.634{col 54}{space 4}-.3351528{col 67}{space 3} .5456244
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .1058697{col 26}{space 2} .1117181{col 37}{space 1}    0.95{col 46}{space 3}0.347{col 54}{space 4}-.1177583{col 67}{space 3} .3294977
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 2.613168{col 26}{space 2}  .283326{col 37}{space 1}    9.22{col 46}{space 3}0.000{col 54}{space 4} 2.046029{col 67}{space 3} 3.180307
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.895748{col 26}{space 2}  .218252{col 37}{space 1}    8.69{col 46}{space 3}0.000{col 54}{space 4} 1.458869{col 67}{space 3} 2.332627
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a10.doc"'"':a10.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a10.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:5}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,494}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:63}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,482.1428}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:58}
{txt}{col 51}{lalign 15:F({res:7}, {res:52})}{col 66} = {res}{ralign 10:61.52}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.2223}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}direct {c |}{col 14}{res}{space 2}-.1654587{col 26}{space 2}  .184049{col 37}{space 1}   -0.90{col 46}{space 3}0.372{col 54}{space 4}-.5338727{col 67}{space 3} .2029554
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.1395505{col 26}{space 2} .0824664{col 37}{space 1}   -1.69{col 46}{space 3}0.096{col 54}{space 4}-.3046249{col 67}{space 3} .0255239
{txt}{space 4}quintall {c |}{col 14}{res}{space 2} .1714354{col 26}{space 2}  .131427{col 37}{space 1}    1.30{col 46}{space 3}0.197{col 54}{space 4}-.0916444{col 67}{space 3} .4345151
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .1281513{col 26}{space 2} .1361289{col 37}{space 1}    0.94{col 46}{space 3}0.350{col 54}{space 4}-.1443404{col 67}{space 3} .4006429
{txt}{space 9}edr {c |}{col 14}{res}{space 2}   -.0353{col 26}{space 2} .2097932{col 37}{space 1}   -0.17{col 46}{space 3}0.867{col 54}{space 4}-.4552467{col 67}{space 3} .3846466
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .2002383{col 26}{space 2} .1074619{col 37}{space 1}    1.86{col 46}{space 3}0.067{col 54}{space 4}-.0148701{col 67}{space 3} .4153467
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 3.689352{col 26}{space 2} .1992922{col 37}{space 1}   18.51{col 46}{space 3}0.000{col 54}{space 4} 3.290425{col 67}{space 3} 4.088279
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.704972{col 26}{space 2} .2220524{col 37}{space 1}    7.68{col 46}{space 3}0.000{col 54}{space 4} 1.260486{col 67}{space 3} 2.149459
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a10.doc"'"':a10.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a10.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:8}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,503}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:65}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,444.2665}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:57}
{txt}{col 51}{lalign 15:F({res:7}, {res:51})}{col 66} = {res}{ralign 10:33.52}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.1128}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}direct {c |}{col 14}{res}{space 2}-.2650509{col 26}{space 2}  .141699{col 37}{space 1}   -1.87{col 46}{space 3}0.067{col 54}{space 4}-.5487983{col 67}{space 3} .0186964
{txt}{space 7}woman {c |}{col 14}{res}{space 2} .1121728{col 26}{space 2} .0859428{col 37}{space 1}    1.31{col 46}{space 3}0.197{col 54}{space 4}-.0599246{col 67}{space 3} .2842703
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}-.0824875{col 26}{space 2}  .159175{col 37}{space 1}   -0.52{col 46}{space 3}0.606{col 54}{space 4}  -.40123{col 67}{space 3} .2362551
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .3292261{col 26}{space 2} .1662585{col 37}{space 1}    1.98{col 46}{space 3}0.053{col 54}{space 4}-.0037009{col 67}{space 3} .6621531
{txt}{space 9}edr {c |}{col 14}{res}{space 2}-.4411314{col 26}{space 2} .2207606{col 37}{space 1}   -2.00{col 46}{space 3}0.050{col 54}{space 4}-.8831968{col 67}{space 3} .0009341
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .2498332{col 26}{space 2} .1347399{col 37}{space 1}    1.85{col 46}{space 3}0.069{col 54}{space 4}-.0199788{col 67}{space 3} .5196452
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 2.751037{col 26}{space 2} .2378206{col 37}{space 1}   11.57{col 46}{space 3}0.000{col 54}{space 4} 2.274809{col 67}{space 3} 3.227264
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  1.73492{col 26}{space 2} .2239444{col 37}{space 1}    7.75{col 46}{space 3}0.000{col 54}{space 4} 1.286479{col 67}{space 3} 2.183361
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a10.doc"'"':a10.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a10.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:6}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,513}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:62}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,467.9819}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:56}
{txt}{col 51}{lalign 15:F({res:7}, {res:50})}{col 66} = {res}{ralign 10:66.44}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.2402}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}direct {c |}{col 14}{res}{space 2}-.3398414{col 26}{space 2} .1654814{col 37}{space 1}   -2.05{col 46}{space 3}0.045{col 54}{space 4}-.6713404{col 67}{space 3}-.0083424
{txt}{space 7}woman {c |}{col 14}{res}{space 2} -.023932{col 26}{space 2} .0910014{col 37}{space 1}   -0.26{col 46}{space 3}0.794{col 54}{space 4}-.2062297{col 67}{space 3} .1583657
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}-.3861937{col 26}{space 2} .1355094{col 37}{space 1}   -2.85{col 46}{space 3}0.006{col 54}{space 4}-.6576517{col 67}{space 3}-.1147357
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2}-.1955699{col 26}{space 2} .1823607{col 37}{space 1}   -1.07{col 46}{space 3}0.288{col 54}{space 4}-.5608823{col 67}{space 3} .1697424
{txt}{space 9}edr {c |}{col 14}{res}{space 2}-.2595943{col 26}{space 2} .2253672{col 37}{space 1}   -1.15{col 46}{space 3}0.254{col 54}{space 4}-.7110591{col 67}{space 3} .1918705
{txt}{space 7}rural {c |}{col 14}{res}{space 2}-.0270532{col 26}{space 2} .1046716{col 37}{space 1}   -0.26{col 46}{space 3}0.797{col 54}{space 4}-.2367356{col 67}{space 3} .1826292
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 4.669369{col 26}{space 2} .2298502{col 37}{space 1}   20.31{col 46}{space 3}0.000{col 54}{space 4} 4.208923{col 67}{space 3} 5.129814
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.413845{col 26}{space 2} .2202223{col 37}{space 1}    6.42{col 46}{space 3}0.000{col 54}{space 4} .9726872{col 67}{space 3} 1.855004
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a10.doc"'"':a10.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a10.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:5}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,453}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:49}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,418.0221}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:44}
{txt}{col 51}{lalign 15:F({res:7}, {res:38})}{col 66} = {res}{ralign 10:11.59}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0840}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}direct {c |}{col 14}{res}{space 2}-1.178329{col 26}{space 2} .3068195{col 37}{space 1}   -3.84{col 46}{space 3}0.000{col 54}{space 4}-1.796683{col 67}{space 3}-.5599753
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.0586745{col 26}{space 2} .1123576{col 37}{space 1}   -0.52{col 46}{space 3}0.604{col 54}{space 4}-.2851164{col 67}{space 3} .1677674
{txt}{space 4}quintall {c |}{col 14}{res}{space 2} .4891014{col 26}{space 2} .2011787{col 37}{space 1}    2.43{col 46}{space 3}0.019{col 54}{space 4} .0836523{col 67}{space 3} .8945504
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .7835043{col 26}{space 2} .1625486{col 37}{space 1}    4.82{col 46}{space 3}0.000{col 54}{space 4} .4559091{col 67}{space 3} 1.111099
{txt}{space 9}edr {c |}{col 14}{res}{space 2} .6113407{col 26}{space 2} .2185771{col 37}{space 1}    2.80{col 46}{space 3}0.008{col 54}{space 4} .1708275{col 67}{space 3} 1.051854
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .0589766{col 26}{space 2} .1385762{col 37}{space 1}    0.43{col 46}{space 3}0.672{col 54}{space 4}-.2203055{col 67}{space 3} .3382586
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 1.387991{col 26}{space 2} .2445325{col 37}{space 1}    5.68{col 46}{space 3}0.000{col 54}{space 4} .8951685{col 67}{space 3} 1.880814
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.129975{col 26}{space 2}  .222853{col 37}{space 1}   14.05{col 46}{space 3}0.000{col 54}{space 4} 2.680844{col 67}{space 3} 3.579106
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a10.doc"'"':a10.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a10.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:4}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,448}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:61}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,440.3183}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:57}
{txt}{col 51}{lalign 15:F({res:7}, {res:51})}{col 66} = {res}{ralign 10:12.23}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0642}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}direct {c |}{col 14}{res}{space 2} .0309408{col 26}{space 2}  .214583{col 37}{space 1}    0.14{col 46}{space 3}0.886{col 54}{space 4}-.3987542{col 67}{space 3} .4606358
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.0407013{col 26}{space 2} .0836346{col 37}{space 1}   -0.49{col 46}{space 3}0.628{col 54}{space 4}-.2081768{col 67}{space 3} .1267741
{txt}{space 4}quintall {c |}{col 14}{res}{space 2} .0016939{col 26}{space 2} .1634227{col 37}{space 1}    0.01{col 46}{space 3}0.992{col 54}{space 4}-.3255544{col 67}{space 3} .3289422
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .3788948{col 26}{space 2}  .186967{col 37}{space 1}    2.03{col 46}{space 3}0.047{col 54}{space 4} .0044998{col 67}{space 3} .7532897
{txt}{space 9}edr {c |}{col 14}{res}{space 2} .3519472{col 26}{space 2} .2121126{col 37}{space 1}    1.66{col 46}{space 3}0.103{col 54}{space 4} -.072801{col 67}{space 3} .7766953
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .7032794{col 26}{space 2} .1334866{col 37}{space 1}    5.27{col 46}{space 3}0.000{col 54}{space 4} .4359771{col 67}{space 3} .9705817
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 1.398929{col 26}{space 2} .2860104{col 37}{space 1}    4.89{col 46}{space 3}0.000{col 54}{space 4} .8262034{col 67}{space 3} 1.971655
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.660189{col 26}{space 2} .2684483{col 37}{space 1}    9.91{col 46}{space 3}0.000{col 54}{space 4} 2.122631{col 67}{space 3} 3.197748
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a10.doc"'"':a10.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a10.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:6}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,428}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:63}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,431.8181}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:57}
{txt}{col 51}{lalign 15:F({res:7}, {res:51})}{col 66} = {res}{ralign 10:16.50}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.1168}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}direct {c |}{col 14}{res}{space 2} .1004645{col 26}{space 2} .1552327{col 37}{space 1}    0.65{col 46}{space 3}0.520{col 54}{space 4}-.2103836{col 67}{space 3} .4113126
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.1102403{col 26}{space 2} .0998727{col 37}{space 1}   -1.10{col 46}{space 3}0.274{col 54}{space 4}-.3102318{col 67}{space 3} .0897513
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}-.0683537{col 26}{space 2} .1364234{col 37}{space 1}   -0.50{col 46}{space 3}0.618{col 54}{space 4}-.3415368{col 67}{space 3} .2048294
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .1075292{col 26}{space 2} .1302846{col 37}{space 1}    0.83{col 46}{space 3}0.413{col 54}{space 4}-.1533612{col 67}{space 3} .3684197
{txt}{space 9}edr {c |}{col 14}{res}{space 2}-.0826279{col 26}{space 2} .1864322{col 37}{space 1}   -0.44{col 46}{space 3}0.659{col 54}{space 4}-.4559519{col 67}{space 3} .2906961
{txt}{space 7}rural {c |}{col 14}{res}{space 2}  .257611{col 26}{space 2} .1158034{col 37}{space 1}    2.22{col 46}{space 3}0.030{col 54}{space 4} .0257186{col 67}{space 3} .4895034
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 2.558529{col 26}{space 2}  .234649{col 37}{space 1}   10.90{col 46}{space 3}0.000{col 54}{space 4} 2.088652{col 67}{space 3} 3.028405
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.767797{col 26}{space 2} .1882012{col 37}{space 1}    9.39{col 46}{space 3}0.000{col 54}{space 4} 1.390931{col 67}{space 3} 2.144664
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a10.doc"'"':a10.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a10.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:3}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,390}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:81}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,400.2687}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:78}
{txt}{col 51}{lalign 15:F({res:7}, {res:72})}{col 66} = {res}{ralign 10:27.13}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.1489}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}direct {c |}{col 14}{res}{space 2} .0298333{col 26}{space 2} .1504852{col 37}{space 1}    0.20{col 46}{space 3}0.843{col 54}{space 4}-.2697596{col 67}{space 3} .3294262
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.0330919{col 26}{space 2} .0903154{col 37}{space 1}   -0.37{col 46}{space 3}0.715{col 54}{space 4} -.212896{col 67}{space 3} .1467122
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}-.1682678{col 26}{space 2} .1565724{col 37}{space 1}   -1.07{col 46}{space 3}0.286{col 54}{space 4}-.4799796{col 67}{space 3}  .143444
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2}-.0844119{col 26}{space 2} .1444503{col 37}{space 1}   -0.58{col 46}{space 3}0.561{col 54}{space 4}-.3719903{col 67}{space 3} .2031664
{txt}{space 9}edr {c |}{col 14}{res}{space 2} .1448728{col 26}{space 2} .2893871{col 37}{space 1}    0.50{col 46}{space 3}0.618{col 54}{space 4}-.4312526{col 67}{space 3} .7209983
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .1581166{col 26}{space 2} .1241883{col 37}{space 1}    1.27{col 46}{space 3}0.207{col 54}{space 4}-.0891234{col 67}{space 3} .4053565
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 3.084823{col 26}{space 2} .2321254{col 37}{space 1}   13.29{col 46}{space 3}0.000{col 54}{space 4} 2.622697{col 67}{space 3} 3.546949
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.359995{col 26}{space 2} .2811526{col 37}{space 1}    8.39{col 46}{space 3}0.000{col 54}{space 4} 1.800263{col 67}{space 3} 2.919727
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a10.doc"'"':a10.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a10.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 3:6}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:2,862}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 3:129}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,401.5189}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:123}
{txt}{col 51}{lalign 15:F({res:7}, {res:117})}{col 66} = {res}{ralign 10:23.19}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.1279}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}direct {c |}{col 14}{res}{space 2} -.118618{col 26}{space 2} .1511368{col 37}{space 1}   -0.78{col 46}{space 3}0.434{col 54}{space 4}-.4177841{col 67}{space 3} .1805481
{txt}{space 7}woman {c |}{col 14}{res}{space 2} -.072884{col 26}{space 2} .0780212{col 37}{space 1}   -0.93{col 46}{space 3}0.352{col 54}{space 4}-.2273222{col 67}{space 3} .0815542
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}-.1418329{col 26}{space 2} .1101037{col 37}{space 1}   -1.29{col 46}{space 3}0.200{col 54}{space 4}-.3597764{col 67}{space 3} .0761106
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2}-.1171028{col 26}{space 2} .0973612{col 37}{space 1}   -1.20{col 46}{space 3}0.231{col 54}{space 4}-.3098233{col 67}{space 3} .0756178
{txt}{space 9}edr {c |}{col 14}{res}{space 2}-.0619248{col 26}{space 2} .1350043{col 37}{space 1}   -0.46{col 46}{space 3}0.647{col 54}{space 4}-.3291574{col 67}{space 3} .2053079
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .3403505{col 26}{space 2}  .087648{col 37}{space 1}    3.88{col 46}{space 3}0.000{col 54}{space 4} .1668566{col 67}{space 3} .5138443
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 2.438842{col 26}{space 2} .1950991{col 37}{space 1}   12.50{col 46}{space 3}0.000{col 54}{space 4} 2.052655{col 67}{space 3} 2.825029
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.506869{col 26}{space 2} .1896643{col 37}{space 1}   13.22{col 46}{space 3}0.000{col 54}{space 4}  2.13144{col 67}{space 3} 2.882298
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a10.doc"'"':a10.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a10.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 3:7}{txt}{col 55}{lalign 15:Number of obs}{col 70} = {res}{ralign 6:1,358}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 3:125}{txt}{col 55}{lalign 15:Population size}{col 70} = {res}{ralign 6:1,358}
{txt}{col 55}{lalign 15:Design df}{col 70} = {res}{ralign 6:118}
{txt}{col 55}{lalign 15:F({res:7}, {res:112})}{col 70} = {res}{ralign 6:15.94}
{txt}{col 55}{lalign 15:Prob > F}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 55}{lalign 15:R-squared}{col 70} = {res}{ralign 6:0.0740}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}direct {c |}{col 14}{res}{space 2}-.2362368{col 26}{space 2} .1643667{col 37}{space 1}   -1.44{col 46}{space 3}0.153{col 54}{space 4}-.5617277{col 67}{space 3} .0892541
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.0207358{col 26}{space 2} .0786431{col 37}{space 1}   -0.26{col 46}{space 3}0.792{col 54}{space 4}-.1764707{col 67}{space 3}  .134999
{txt}{space 4}quintall {c |}{col 14}{res}{space 2} .2226127{col 26}{space 2} .1832339{col 37}{space 1}    1.21{col 46}{space 3}0.227{col 54}{space 4}-.1402403{col 67}{space 3} .5854656
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2}  .060554{col 26}{space 2} .1497722{col 37}{space 1}    0.40{col 46}{space 3}0.687{col 54}{space 4}-.2360356{col 67}{space 3} .3571437
{txt}{space 9}edr {c |}{col 14}{res}{space 2} .2309111{col 26}{space 2} .2190607{col 37}{space 1}    1.05{col 46}{space 3}0.294{col 54}{space 4}-.2028887{col 67}{space 3} .6647109
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .1677649{col 26}{space 2} .1662295{col 37}{space 1}    1.01{col 46}{space 3}0.315{col 54}{space 4}-.1614148{col 67}{space 3} .4969445
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 2.356282{col 26}{space 2} .2429381{col 37}{space 1}    9.70{col 46}{space 3}0.000{col 54}{space 4} 1.875198{col 67}{space 3} 2.837366
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.300208{col 26}{space 2} .2228412{col 37}{space 1}   10.32{col 46}{space 3}0.000{col 54}{space 4} 1.858922{col 67}{space 3} 2.741495
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a10.doc"'"':a10.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a10.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:6}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,360}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:63}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,357.2855}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:57}
{txt}{col 51}{lalign 15:F({res:7}, {res:51})}{col 66} = {res}{ralign 10:23.60}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.1071}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}direct {c |}{col 14}{res}{space 2}  .103669{col 26}{space 2}  .191948{col 37}{space 1}    0.54{col 46}{space 3}0.591{col 54}{space 4}-.2807002{col 67}{space 3} .4880381
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.1050288{col 26}{space 2} .1115047{col 37}{space 1}   -0.94{col 46}{space 3}0.350{col 54}{space 4}-.3283132{col 67}{space 3} .1182556
{txt}{space 4}quintall {c |}{col 14}{res}{space 2} .1292745{col 26}{space 2} .1988996{col 37}{space 1}    0.65{col 46}{space 3}0.518{col 54}{space 4}-.2690151{col 67}{space 3} .5275642
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .3693996{col 26}{space 2} .1732177{col 37}{space 1}    2.13{col 46}{space 3}0.037{col 54}{space 4} .0225372{col 67}{space 3}  .716262
{txt}{space 9}edr {c |}{col 14}{res}{space 2} .2947505{col 26}{space 2} .2390669{col 37}{space 1}    1.23{col 46}{space 3}0.223{col 54}{space 4}-.1839728{col 67}{space 3} .7734738
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .3552171{col 26}{space 2} .1078915{col 37}{space 1}    3.29{col 46}{space 3}0.002{col 54}{space 4} .1391681{col 67}{space 3} .5712661
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 2.638878{col 26}{space 2} .2128114{col 37}{space 1}   12.40{col 46}{space 3}0.000{col 54}{space 4} 2.212731{col 67}{space 3} 3.065026
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.830995{col 26}{space 2} .2247341{col 37}{space 1}    8.15{col 46}{space 3}0.000{col 54}{space 4} 1.380973{col 67}{space 3} 2.281017
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a10.doc"'"':a10.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a10.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 3:9}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,314}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 3:123}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,250.5389}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:114}
{txt}{col 51}{lalign 15:F({res:7}, {res:108})}{col 66} = {res}{ralign 10:5.34}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0763}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}direct {c |}{col 14}{res}{space 2}-.1272746{col 26}{space 2} .4262051{col 37}{space 1}   -0.30{col 46}{space 3}0.766{col 54}{space 4}-.9715837{col 67}{space 3} .7170345
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.0680089{col 26}{space 2} .1105651{col 37}{space 1}   -0.62{col 46}{space 3}0.540{col 54}{space 4}-.2870376{col 67}{space 3} .1510198
{txt}{space 4}quintall {c |}{col 14}{res}{space 2} .2139659{col 26}{space 2} .1981654{col 37}{space 1}    1.08{col 46}{space 3}0.283{col 54}{space 4}-.1785983{col 67}{space 3} .6065301
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .9580675{col 26}{space 2} .2100255{col 37}{space 1}    4.56{col 46}{space 3}0.000{col 54}{space 4} .5420086{col 67}{space 3} 1.374126
{txt}{space 9}edr {c |}{col 14}{res}{space 2} .5417981{col 26}{space 2} .2955153{col 37}{space 1}    1.83{col 46}{space 3}0.069{col 54}{space 4}-.0436154{col 67}{space 3} 1.127212
{txt}{space 7}rural {c |}{col 14}{res}{space 2} -.005759{col 26}{space 2} .1937683{col 37}{space 1}   -0.03{col 46}{space 3}0.976{col 54}{space 4}-.3896126{col 67}{space 3} .3780946
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 1.505182{col 26}{space 2} .3056898{col 37}{space 1}    4.92{col 46}{space 3}0.000{col 54}{space 4} .8996125{col 67}{space 3} 2.110751
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  2.94391{col 26}{space 2} .3736052{col 37}{space 1}    7.88{col 46}{space 3}0.000{col 54}{space 4} 2.203801{col 67}{space 3} 3.684019
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a10.doc"'"':a10.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a10.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:2}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,472}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:63}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,460.3174}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:61}
{txt}{col 51}{lalign 15:F({res:7}, {res:55})}{col 66} = {res}{ralign 10:37.33}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.1385}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}direct {c |}{col 14}{res}{space 2}-.2914831{col 26}{space 2} .2865881{col 37}{space 1}   -1.02{col 46}{space 3}0.313{col 54}{space 4}-.8645513{col 67}{space 3} .2815851
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.3175791{col 26}{space 2} .0811398{col 37}{space 1}   -3.91{col 46}{space 3}0.000{col 54}{space 4}-.4798282{col 67}{space 3}  -.15533
{txt}{space 4}quintall {c |}{col 14}{res}{space 2} .1720431{col 26}{space 2} .1417184{col 37}{space 1}    1.21{col 46}{space 3}0.229{col 54}{space 4}-.1113403{col 67}{space 3} .4554265
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} 1.076127{col 26}{space 2} .1111423{col 37}{space 1}    9.68{col 46}{space 3}0.000{col 54}{space 4}  .853884{col 67}{space 3}  1.29837
{txt}{space 9}edr {c |}{col 14}{res}{space 2} 1.066544{col 26}{space 2} .1937594{col 37}{space 1}    5.50{col 46}{space 3}0.000{col 54}{space 4} .6790985{col 67}{space 3}  1.45399
{txt}{space 7}rural {c |}{col 14}{res}{space 2}-.1665273{col 26}{space 2} .1570816{col 37}{space 1}   -1.06{col 46}{space 3}0.293{col 54}{space 4}-.4806312{col 67}{space 3} .1475767
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 1.719799{col 26}{space 2} .1768978{col 37}{space 1}    9.72{col 46}{space 3}0.000{col 54}{space 4}  1.36607{col 67}{space 3} 2.073528
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.302506{col 26}{space 2}  .191289{col 37}{space 1}   17.26{col 46}{space 3}0.000{col 54}{space 4}     2.92{col 67}{space 3} 3.685012
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a10.doc"'"':a10.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a10.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 3:5}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,471}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 3:125}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,475.4812}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:120}
{txt}{col 51}{lalign 15:F({res:7}, {res:114})}{col 66} = {res}{ralign 10:21.35}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.1161}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}direct {c |}{col 14}{res}{space 2}-.3235165{col 26}{space 2} .1670709{col 37}{space 1}   -1.94{col 46}{space 3}0.055{col 54}{space 4}-.6543053{col 67}{space 3} .0072723
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.0479533{col 26}{space 2} .0925907{col 37}{space 1}   -0.52{col 46}{space 3}0.605{col 54}{space 4}-.2312765{col 67}{space 3}   .13537
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}-.2098151{col 26}{space 2} .1623579{col 37}{space 1}   -1.29{col 46}{space 3}0.199{col 54}{space 4}-.5312725{col 67}{space 3} .1116423
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .3425467{col 26}{space 2} .1794614{col 37}{space 1}    1.91{col 46}{space 3}0.059{col 54}{space 4}-.0127743{col 67}{space 3} .6978677
{txt}{space 9}edr {c |}{col 14}{res}{space 2} .3988534{col 26}{space 2} .2790823{col 37}{space 1}    1.43{col 46}{space 3}0.156{col 54}{space 4}-.1537101{col 67}{space 3} .9514169
{txt}{space 7}rural {c |}{col 14}{res}{space 2}  .372393{col 26}{space 2} .1691788{col 37}{space 1}    2.20{col 46}{space 3}0.030{col 54}{space 4} .0374308{col 67}{space 3} .7073552
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 2.338548{col 26}{space 2} .2147026{col 37}{space 1}   10.89{col 46}{space 3}0.000{col 54}{space 4} 1.913452{col 67}{space 3} 2.763645
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.507184{col 26}{space 2} .2228338{col 37}{space 1}    6.76{col 46}{space 3}0.000{col 54}{space 4} 1.065989{col 67}{space 3}  1.94838
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a10.doc"'"':a10.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a10.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:8}{txt}{col 55}{lalign 15:Number of obs}{col 70} = {res}{ralign 6:1,402}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:83}{txt}{col 55}{lalign 15:Population size}{col 70} = {res}{ralign 6:1,402}
{txt}{col 55}{lalign 15:Design df}{col 70} = {res}{ralign 6:75}
{txt}{col 55}{lalign 15:F({res:7}, {res:69})}{col 70} = {res}{ralign 6:83.80}
{txt}{col 55}{lalign 15:Prob > F}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 55}{lalign 15:R-squared}{col 70} = {res}{ralign 6:0.4081}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}direct {c |}{col 14}{res}{space 2} .1832116{col 26}{space 2} .2692965{col 37}{space 1}    0.68{col 46}{space 3}0.498{col 54}{space 4}-.3532545{col 67}{space 3} .7196777
{txt}{space 7}woman {c |}{col 14}{res}{space 2} -.154366{col 26}{space 2} .0768537{col 37}{space 1}   -2.01{col 46}{space 3}0.048{col 54}{space 4}-.3074664{col 67}{space 3}-.0012656
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}-.0635283{col 26}{space 2} .1404795{col 37}{space 1}   -0.45{col 46}{space 3}0.652{col 54}{space 4}-.3433779{col 67}{space 3} .2163213
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2}  .240293{col 26}{space 2} .1687152{col 37}{space 1}    1.42{col 46}{space 3}0.159{col 54}{space 4} -.095805{col 67}{space 3}  .576391
{txt}{space 9}edr {c |}{col 14}{res}{space 2} .0828242{col 26}{space 2} .2877071{col 37}{space 1}    0.29{col 46}{space 3}0.774{col 54}{space 4}-.4903179{col 67}{space 3} .6559662
{txt}{space 7}rural {c |}{col 14}{res}{space 2}-.1283291{col 26}{space 2} .2034371{col 37}{space 1}   -0.63{col 46}{space 3}0.530{col 54}{space 4}-.5335966{col 67}{space 3} .2769384
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 4.592806{col 26}{space 2} .1951086{col 37}{space 1}   23.54{col 46}{space 3}0.000{col 54}{space 4}  4.20413{col 67}{space 3} 4.981482
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.003321{col 26}{space 2} .2588207{col 37}{space 1}    7.74{col 46}{space 3}0.000{col 54}{space 4} 1.487724{col 67}{space 3} 2.518919
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a10.doc"'"':a10.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a10.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:7}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,360}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:84}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,349.2063}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:77}
{txt}{col 51}{lalign 15:F({res:7}, {res:71})}{col 66} = {res}{ralign 10:26.41}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.1332}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}direct {c |}{col 14}{res}{space 2}-.5180166{col 26}{space 2} .3177792{col 37}{space 1}   -1.63{col 46}{space 3}0.107{col 54}{space 4}-1.150796{col 67}{space 3} .1147626
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.1079226{col 26}{space 2} .0973512{col 37}{space 1}   -1.11{col 46}{space 3}0.271{col 54}{space 4}-.3017736{col 67}{space 3} .0859284
{txt}{space 4}quintall {c |}{col 14}{res}{space 2} .2341247{col 26}{space 2} .1610538{col 37}{space 1}    1.45{col 46}{space 3}0.150{col 54}{space 4}-.0865744{col 67}{space 3} .5548237
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .8376552{col 26}{space 2} .1488678{col 37}{space 1}    5.63{col 46}{space 3}0.000{col 54}{space 4} .5412216{col 67}{space 3} 1.134089
{txt}{space 9}edr {c |}{col 14}{res}{space 2} .5106944{col 26}{space 2} .2402821{col 37}{space 1}    2.13{col 46}{space 3}0.037{col 54}{space 4} .0322316{col 67}{space 3} .9891572
{txt}{space 7}rural {c |}{col 14}{res}{space 2}-.0258504{col 26}{space 2} .2156204{col 37}{space 1}   -0.12{col 46}{space 3}0.905{col 54}{space 4}-.4552054{col 67}{space 3} .4035047
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 2.421852{col 26}{space 2} .1978723{col 37}{space 1}   12.24{col 46}{space 3}0.000{col 54}{space 4} 2.027838{col 67}{space 3} 2.815866
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.540249{col 26}{space 2}  .205105{col 37}{space 1}   12.39{col 46}{space 3}0.000{col 54}{space 4} 2.131832{col 67}{space 3} 2.948665
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a10.doc"'"':a10.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a10.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:4}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,475}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:63}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,455.5921}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:59}
{txt}{col 51}{lalign 15:F({res:7}, {res:53})}{col 66} = {res}{ralign 10:20.36}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0820}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}direct {c |}{col 14}{res}{space 2}-.6166261{col 26}{space 2} .1059956{col 37}{space 1}   -5.82{col 46}{space 3}0.000{col 54}{space 4}-.8287228{col 67}{space 3}-.4045294
{txt}{space 7}woman {c |}{col 14}{res}{space 2} .0529525{col 26}{space 2} .0876825{col 37}{space 1}    0.60{col 46}{space 3}0.548{col 54}{space 4}-.1224997{col 67}{space 3} .2284048
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}-.0801636{col 26}{space 2} .1561889{col 37}{space 1}   -0.51{col 46}{space 3}0.610{col 54}{space 4}-.3926968{col 67}{space 3} .2323697
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2}-.3990389{col 26}{space 2}  .196325{col 37}{space 1}   -2.03{col 46}{space 3}0.047{col 54}{space 4}-.7918842{col 67}{space 3}-.0061936
{txt}{space 9}edr {c |}{col 14}{res}{space 2}-.1474238{col 26}{space 2} .2313228{col 37}{space 1}   -0.64{col 46}{space 3}0.526{col 54}{space 4}-.6102996{col 67}{space 3}  .315452
{txt}{space 7}rural {c |}{col 14}{res}{space 2}   .16023{col 26}{space 2} .1027374{col 37}{space 1}    1.56{col 46}{space 3}0.124{col 54}{space 4}-.0453471{col 67}{space 3} .3658071
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 2.271179{col 26}{space 2} .2512707{col 37}{space 1}    9.04{col 46}{space 3}0.000{col 54}{space 4} 1.768388{col 67}{space 3} 2.773971
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.237978{col 26}{space 2} .2743488{col 37}{space 1}    8.16{col 46}{space 3}0.000{col 54}{space 4} 1.689007{col 67}{space 3} 2.786948
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a10.doc"'"':a10.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a10.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:5}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,285}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:62}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,274.8015}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:57}
{txt}{col 51}{lalign 15:F({res:7}, {res:51})}{col 66} = {res}{ralign 10:5.76}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0001}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0437}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}direct {c |}{col 14}{res}{space 2} .4442612{col 26}{space 2} .1767774{col 37}{space 1}    2.51{col 46}{space 3}0.015{col 54}{space 4} .0902704{col 67}{space 3} .7982519
{txt}{space 7}woman {c |}{col 14}{res}{space 2} -.087995{col 26}{space 2} .0757478{col 37}{space 1}   -1.16{col 46}{space 3}0.250{col 54}{space 4}-.2396773{col 67}{space 3} .0636873
{txt}{space 4}quintall {c |}{col 14}{res}{space 2} .4063049{col 26}{space 2}  .165635{col 37}{space 1}    2.45{col 46}{space 3}0.017{col 54}{space 4} .0746265{col 67}{space 3} .7379834
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2}-.1639166{col 26}{space 2} .1588221{col 37}{space 1}   -1.03{col 46}{space 3}0.306{col 54}{space 4}-.4819524{col 67}{space 3} .1541193
{txt}{space 9}edr {c |}{col 14}{res}{space 2}-.6176419{col 26}{space 2} .2897148{col 37}{space 1}   -2.13{col 46}{space 3}0.037{col 54}{space 4}-1.197786{col 67}{space 3} -.037498
{txt}{space 7}rural {c |}{col 14}{res}{space 2}-.2706304{col 26}{space 2} .1240475{col 37}{space 1}   -2.18{col 46}{space 3}0.033{col 54}{space 4}-.5190313{col 67}{space 3}-.0222296
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} .8686305{col 26}{space 2} .1872521{col 37}{space 1}    4.64{col 46}{space 3}0.000{col 54}{space 4} .4936645{col 67}{space 3} 1.243596
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.571717{col 26}{space 2} .2289064{col 37}{space 1}   11.23{col 46}{space 3}0.000{col 54}{space 4} 2.113339{col 67}{space 3} 3.030094
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a10.doc"'"':a10.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a10.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:4}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,406}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:58}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,403.1937}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:54}
{txt}{col 51}{lalign 15:F({res:7}, {res:48})}{col 66} = {res}{ralign 10:13.30}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0899}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}direct {c |}{col 14}{res}{space 2}-.2474346{col 26}{space 2} .1688614{col 37}{space 1}   -1.47{col 46}{space 3}0.149{col 54}{space 4}-.5859814{col 67}{space 3} .0911122
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.0341715{col 26}{space 2} .0952022{col 37}{space 1}   -0.36{col 46}{space 3}0.721{col 54}{space 4}-.2250403{col 67}{space 3} .1566973
{txt}{space 4}quintall {c |}{col 14}{res}{space 2} .1435844{col 26}{space 2} .1585127{col 37}{space 1}    0.91{col 46}{space 3}0.369{col 54}{space 4}-.1742143{col 67}{space 3} .4613832
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .4275234{col 26}{space 2} .1444372{col 37}{space 1}    2.96{col 46}{space 3}0.005{col 54}{space 4} .1379442{col 67}{space 3} .7171026
{txt}{space 9}edr {c |}{col 14}{res}{space 2} .0222219{col 26}{space 2} .2771415{col 37}{space 1}    0.08{col 46}{space 3}0.936{col 54}{space 4}-.5334133{col 67}{space 3}  .577857
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .0137882{col 26}{space 2} .1042299{col 37}{space 1}    0.13{col 46}{space 3}0.895{col 54}{space 4}-.1951803{col 67}{space 3} .2227566
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 1.777873{col 26}{space 2} .2174371{col 37}{space 1}    8.18{col 46}{space 3}0.000{col 54}{space 4} 1.341937{col 67}{space 3} 2.213808
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.152169{col 26}{space 2} .2372512{col 37}{space 1}    9.07{col 46}{space 3}0.000{col 54}{space 4} 1.676509{col 67}{space 3} 2.627829
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a10.doc"'"':a10.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a10.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:4}{txt}{col 52}{lalign 15:Number of obs}{col 67} = {res}{ralign 9:1,437}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:51}{txt}{col 52}{lalign 15:Population size}{col 67} = {res}{ralign 9:1,384.393}
{txt}{col 52}{lalign 15:Design df}{col 67} = {res}{ralign 9:47}
{txt}{col 52}{lalign 15:F({res:7}, {res:41})}{col 67} = {res}{ralign 9:27.12}
{txt}{col 52}{lalign 15:Prob > F}{col 67} = {res}{ralign 9:0.0000}
{txt}{col 52}{lalign 15:R-squared}{col 67} = {res}{ralign 9:0.1665}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}direct {c |}{col 14}{res}{space 2}-.0449429{col 26}{space 2} .2927654{col 37}{space 1}   -0.15{col 46}{space 3}0.879{col 54}{space 4}-.6339108{col 67}{space 3}  .544025
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.1743072{col 26}{space 2} .0738364{col 37}{space 1}   -2.36{col 46}{space 3}0.022{col 54}{space 4}-.3228469{col 67}{space 3}-.0257676
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}-.2003661{col 26}{space 2} .1569306{col 37}{space 1}   -1.28{col 46}{space 3}0.208{col 54}{space 4}-.5160698{col 67}{space 3} .1153375
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2}-.0227699{col 26}{space 2} .1623261{col 37}{space 1}   -0.14{col 46}{space 3}0.889{col 54}{space 4}-.3493278{col 67}{space 3}  .303788
{txt}{space 9}edr {c |}{col 14}{res}{space 2}-.5054669{col 26}{space 2} .2881578{col 37}{space 1}   -1.75{col 46}{space 3}0.086{col 54}{space 4}-1.085166{col 67}{space 3} .0742318
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .3039705{col 26}{space 2} .1753872{col 37}{space 1}    1.73{col 46}{space 3}0.090{col 54}{space 4} -.048863{col 67}{space 3}  .656804
{txt}{space 10}m1 {c |}{col 14}{res}{space 2}   2.7607{col 26}{space 2} .2168354{col 37}{space 1}   12.73{col 46}{space 3}0.000{col 54}{space 4} 2.324484{col 67}{space 3} 3.196917
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.389249{col 26}{space 2} .2439928{col 37}{space 1}    9.79{col 46}{space 3}0.000{col 54}{space 4} 1.898399{col 67}{space 3} 2.880099
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a10.doc"'"':a10.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a10.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:6}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,465}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:63}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,433.4638}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:57}
{txt}{col 51}{lalign 15:F({res:7}, {res:51})}{col 66} = {res}{ralign 10:13.67}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0887}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}direct {c |}{col 14}{res}{space 2}-.2409779{col 26}{space 2} .1287515{col 37}{space 1}   -1.87{col 46}{space 3}0.066{col 54}{space 4}-.4987982{col 67}{space 3} .0168424
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.1550596{col 26}{space 2}  .075821{col 37}{space 1}   -2.05{col 46}{space 3}0.045{col 54}{space 4}-.3068885{col 67}{space 3}-.0032307
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}-.3577641{col 26}{space 2}  .145091{col 37}{space 1}   -2.47{col 46}{space 3}0.017{col 54}{space 4}-.6483038{col 67}{space 3}-.0672245
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .4591583{col 26}{space 2} .1746959{col 37}{space 1}    2.63{col 46}{space 3}0.011{col 54}{space 4} .1093357{col 67}{space 3} .8089808
{txt}{space 9}edr {c |}{col 14}{res}{space 2} .1416987{col 26}{space 2} .2381696{col 37}{space 1}    0.59{col 46}{space 3}0.554{col 54}{space 4}-.3352277{col 67}{space 3} .6186252
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .2479727{col 26}{space 2} .1038316{col 37}{space 1}    2.39{col 46}{space 3}0.020{col 54}{space 4} .0400536{col 67}{space 3} .4558919
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 2.064473{col 26}{space 2} .2336653{col 37}{space 1}    8.84{col 46}{space 3}0.000{col 54}{space 4} 1.596567{col 67}{space 3}  2.53238
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.596644{col 26}{space 2} .2150757{col 37}{space 1}   12.07{col 46}{space 3}0.000{col 54}{space 4} 2.165963{col 67}{space 3} 3.027326
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a10.doc"'"':a10.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a10.txt""':seeout}

{com}.         
. ***A11: country-by-country analysis for H1, indirect
. svy: reg trustel indirect woman quintall agecohort edr rural m1 if wave==2014 & pais==1
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 3:4}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,427}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 3:130}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,394.4626}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:126}
{txt}{col 51}{lalign 15:F({res:7}, {res:120})}{col 66} = {res}{ralign 10:51.70}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.2327}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}     trustel{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}indirect {c |}{col 14}{res}{space 2}-.4694441{col 26}{space 2} .1073388{col 37}{space 1}   -4.37{col 46}{space 3}0.000{col 54}{space 4}-.6818644{col 67}{space 3}-.2570237
{txt}{space 7}woman {c |}{col 14}{res}{space 2} .0060389{col 26}{space 2} .0929814{col 37}{space 1}    0.06{col 46}{space 3}0.948{col 54}{space 4}-.1779686{col 67}{space 3} .1900464
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}-.1699835{col 26}{space 2} .1470459{col 37}{space 1}   -1.16{col 46}{space 3}0.250{col 54}{space 4}-.4609831{col 67}{space 3} .1210161
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .2397553{col 26}{space 2} .1444735{col 37}{space 1}    1.66{col 46}{space 3}0.100{col 54}{space 4}-.0461536{col 67}{space 3} .5256642
{txt}{space 9}edr {c |}{col 14}{res}{space 2}-.1172525{col 26}{space 2} .2335094{col 37}{space 1}   -0.50{col 46}{space 3}0.616{col 54}{space 4}-.5793607{col 67}{space 3} .3448557
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .3615981{col 26}{space 2} .1858914{col 37}{space 1}    1.95{col 46}{space 3}0.054{col 54}{space 4}-.0062756{col 67}{space 3} .7294717
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 2.940389{col 26}{space 2} .1855655{col 37}{space 1}   15.85{col 46}{space 3}0.000{col 54}{space 4}  2.57316{col 67}{space 3} 3.307617
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.144739{col 26}{space 2} .2143554{col 37}{space 1}   10.01{col 46}{space 3}0.000{col 54}{space 4} 1.720536{col 67}{space 3} 2.568942
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using a11.doc, dec(2) replace ctitle(Mexico)
{txt}{stata `"shellout using `"a11.doc"'"':a11.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a11.txt""':seeout}

{com}. levelsof pais if wave==2014 & !missing(trustel, indirect) & pais>1, local(2014indirect)
{res}{txt}2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 21 22 23 24 26

{com}. foreach x in `2014indirect' {c -(}
{txt}  2{com}.                 svy: reg b47a indirect woman quintall agecohort edr rural m1 if wave==2014 & pais==`x'
{txt}  3{com}.                 outreg2 using a11.doc, dec(2) append ctitle(`x')
{txt}  4{com}. {c )-}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:4}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,401}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:62}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,395.4184}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:58}
{txt}{col 51}{lalign 15:F({res:7}, {res:52})}{col 66} = {res}{ralign 10:12.49}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0716}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}indirect {c |}{col 14}{res}{space 2} .1818676{col 26}{space 2} .1539667{col 37}{space 1}    1.18{col 46}{space 3}0.242{col 54}{space 4}-.1263303{col 67}{space 3} .4900655
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.0623834{col 26}{space 2} .0846997{col 37}{space 1}   -0.74{col 46}{space 3}0.464{col 54}{space 4}-.2319284{col 67}{space 3} .1071616
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}-.2735398{col 26}{space 2} .1666036{col 37}{space 1}   -1.64{col 46}{space 3}0.106{col 54}{space 4}-.6070331{col 67}{space 3} .0599535
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .2364649{col 26}{space 2} .1485539{col 37}{space 1}    1.59{col 46}{space 3}0.117{col 54}{space 4} -.060898{col 67}{space 3} .5338277
{txt}{space 9}edr {c |}{col 14}{res}{space 2} .0972617{col 26}{space 2} .2130536{col 37}{space 1}    0.46{col 46}{space 3}0.650{col 54}{space 4}-.3292113{col 67}{space 3} .5237348
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .1322644{col 26}{space 2} .1113264{col 37}{space 1}    1.19{col 46}{space 3}0.240{col 54}{space 4}-.0905797{col 67}{space 3} .3551084
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 2.605395{col 26}{space 2} .2795856{col 37}{space 1}    9.32{col 46}{space 3}0.000{col 54}{space 4} 2.045744{col 67}{space 3} 3.165047
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.935724{col 26}{space 2} .2212368{col 37}{space 1}    8.75{col 46}{space 3}0.000{col 54}{space 4} 1.492871{col 67}{space 3} 2.378578
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a11.doc"'"':a11.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a11.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:5}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,483}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:63}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,471.2301}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:58}
{txt}{col 51}{lalign 15:F({res:7}, {res:52})}{col 66} = {res}{ralign 10:59.93}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.2216}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}indirect {c |}{col 14}{res}{space 2}-.1057454{col 26}{space 2} .1133198{col 37}{space 1}   -0.93{col 46}{space 3}0.355{col 54}{space 4}-.3325795{col 67}{space 3} .1210888
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.1427433{col 26}{space 2} .0830938{col 37}{space 1}   -1.72{col 46}{space 3}0.091{col 54}{space 4}-.3090737{col 67}{space 3} .0235871
{txt}{space 4}quintall {c |}{col 14}{res}{space 2} .1652145{col 26}{space 2} .1304481{col 37}{space 1}    1.27{col 46}{space 3}0.210{col 54}{space 4}-.0959058{col 67}{space 3} .4263347
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .1194033{col 26}{space 2}  .139032{col 37}{space 1}    0.86{col 46}{space 3}0.394{col 54}{space 4}-.1588994{col 67}{space 3} .3977061
{txt}{space 9}edr {c |}{col 14}{res}{space 2}-.0393233{col 26}{space 2} .2085463{col 37}{space 1}   -0.19{col 46}{space 3}0.851{col 54}{space 4} -.456774{col 67}{space 3} .3781275
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .2115058{col 26}{space 2} .1084874{col 37}{space 1}    1.95{col 46}{space 3}0.056{col 54}{space 4}-.0056554{col 67}{space 3}  .428667
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 3.696693{col 26}{space 2}  .202378{col 37}{space 1}   18.27{col 46}{space 3}0.000{col 54}{space 4} 3.291589{col 67}{space 3} 4.101796
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.710325{col 26}{space 2} .2244798{col 37}{space 1}    7.62{col 46}{space 3}0.000{col 54}{space 4} 1.260979{col 67}{space 3}  2.15967
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a11.doc"'"':a11.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a11.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:8}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,506}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:65}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,447.1493}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:57}
{txt}{col 51}{lalign 15:F({res:7}, {res:51})}{col 66} = {res}{ralign 10:33.56}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.1163}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}indirect {c |}{col 14}{res}{space 2}-.3959436{col 26}{space 2} .1320794{col 37}{space 1}   -3.00{col 46}{space 3}0.004{col 54}{space 4}-.6604281{col 67}{space 3} -.131459
{txt}{space 7}woman {c |}{col 14}{res}{space 2} .1100261{col 26}{space 2} .0878919{col 37}{space 1}    1.25{col 46}{space 3}0.216{col 54}{space 4}-.0659743{col 67}{space 3} .2860266
{txt}{space 4}quintall {c |}{col 14}{res}{space 2} -.104565{col 26}{space 2} .1570797{col 37}{space 1}   -0.67{col 46}{space 3}0.508{col 54}{space 4}-.4191117{col 67}{space 3} .2099817
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2}  .301987{col 26}{space 2} .1698837{col 37}{space 1}    1.78{col 46}{space 3}0.081{col 54}{space 4}-.0381991{col 67}{space 3} .6421732
{txt}{space 9}edr {c |}{col 14}{res}{space 2}-.3905518{col 26}{space 2} .2180621{col 37}{space 1}   -1.79{col 46}{space 3}0.079{col 54}{space 4}-.8272136{col 67}{space 3}   .04611
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .2684672{col 26}{space 2} .1337259{col 37}{space 1}    2.01{col 46}{space 3}0.049{col 54}{space 4} .0006856{col 67}{space 3} .5362488
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 2.734625{col 26}{space 2} .2355578{col 37}{space 1}   11.61{col 46}{space 3}0.000{col 54}{space 4} 2.262928{col 67}{space 3} 3.206321
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.774567{col 26}{space 2} .2216066{col 37}{space 1}    8.01{col 46}{space 3}0.000{col 54}{space 4} 1.330807{col 67}{space 3} 2.218326
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a11.doc"'"':a11.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a11.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:6}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,508}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:62}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,463.1306}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:56}
{txt}{col 51}{lalign 15:F({res:7}, {res:50})}{col 66} = {res}{ralign 10:65.11}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.2393}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}indirect {c |}{col 14}{res}{space 2}-.0560765{col 26}{space 2} .1654513{col 37}{space 1}   -0.34{col 46}{space 3}0.736{col 54}{space 4}-.3875152{col 67}{space 3} .2753622
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.0019913{col 26}{space 2} .0926537{col 37}{space 1}   -0.02{col 46}{space 3}0.983{col 54}{space 4} -.187599{col 67}{space 3} .1836164
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}-.3815758{col 26}{space 2} .1360535{col 37}{space 1}   -2.80{col 46}{space 3}0.007{col 54}{space 4}-.6541237{col 67}{space 3}-.1090279
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2}-.1921802{col 26}{space 2} .1846404{col 37}{space 1}   -1.04{col 46}{space 3}0.302{col 54}{space 4}-.5620593{col 67}{space 3}  .177699
{txt}{space 9}edr {c |}{col 14}{res}{space 2}-.2412121{col 26}{space 2}  .226712{col 37}{space 1}   -1.06{col 46}{space 3}0.292{col 54}{space 4}-.6953707{col 67}{space 3} .2129465
{txt}{space 7}rural {c |}{col 14}{res}{space 2}-.0248652{col 26}{space 2} .1044839{col 37}{space 1}   -0.24{col 46}{space 3}0.813{col 54}{space 4}-.2341715{col 67}{space 3} .1844412
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 4.722014{col 26}{space 2} .2216922{col 37}{space 1}   21.30{col 46}{space 3}0.000{col 54}{space 4} 4.277911{col 67}{space 3} 5.166116
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.338339{col 26}{space 2} .2181613{col 37}{space 1}    6.13{col 46}{space 3}0.000{col 54}{space 4} .9013095{col 67}{space 3} 1.775369
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a11.doc"'"':a11.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a11.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:5}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,446}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:49}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,411.1906}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:44}
{txt}{col 51}{lalign 15:F({res:7}, {res:38})}{col 66} = {res}{ralign 10:10.84}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0837}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}indirect {c |}{col 14}{res}{space 2} -1.09685{col 26}{space 2} .2594518{col 37}{space 1}   -4.23{col 46}{space 3}0.000{col 54}{space 4}-1.619741{col 67}{space 3}-.5739595
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.0671587{col 26}{space 2} .1114697{col 37}{space 1}   -0.60{col 46}{space 3}0.550{col 54}{space 4}-.2918112{col 67}{space 3} .1574938
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}  .505264{col 26}{space 2}   .19895{col 37}{space 1}    2.54{col 46}{space 3}0.015{col 54}{space 4} .1043067{col 67}{space 3} .9062213
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .7654449{col 26}{space 2} .1540519{col 37}{space 1}    4.97{col 46}{space 3}0.000{col 54}{space 4} .4549736{col 67}{space 3} 1.075916
{txt}{space 9}edr {c |}{col 14}{res}{space 2} .6199215{col 26}{space 2} .2125475{col 37}{space 1}    2.92{col 46}{space 3}0.006{col 54}{space 4} .1915601{col 67}{space 3} 1.048283
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .0325689{col 26}{space 2} .1421889{col 37}{space 1}    0.23{col 46}{space 3}0.820{col 54}{space 4}-.2539939{col 67}{space 3} .3191317
{txt}{space 10}m1 {c |}{col 14}{res}{space 2}  1.42329{col 26}{space 2}   .23677{col 37}{space 1}    6.01{col 46}{space 3}0.000{col 54}{space 4} .9461119{col 67}{space 3} 1.900469
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.123323{col 26}{space 2} .2222132{col 37}{space 1}   14.06{col 46}{space 3}0.000{col 54}{space 4} 2.675482{col 67}{space 3} 3.571164
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a11.doc"'"':a11.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a11.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:4}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,444}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:61}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,436.3395}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:57}
{txt}{col 51}{lalign 15:F({res:7}, {res:51})}{col 66} = {res}{ralign 10:11.38}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0631}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}indirect {c |}{col 14}{res}{space 2}-.0174679{col 26}{space 2} .2602424{col 37}{space 1}   -0.07{col 46}{space 3}0.947{col 54}{space 4}-.5385942{col 67}{space 3} .5036585
{txt}{space 7}woman {c |}{col 14}{res}{space 2} -.058753{col 26}{space 2} .0879915{col 37}{space 1}   -0.67{col 46}{space 3}0.507{col 54}{space 4} -.234953{col 67}{space 3}  .117447
{txt}{space 4}quintall {c |}{col 14}{res}{space 2} .0076106{col 26}{space 2}   .16672{col 37}{space 1}    0.05{col 46}{space 3}0.964{col 54}{space 4}-.3262406{col 67}{space 3} .3414617
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .3846967{col 26}{space 2}  .181309{col 37}{space 1}    2.12{col 46}{space 3}0.038{col 54}{space 4} .0216318{col 67}{space 3} .7477616
{txt}{space 9}edr {c |}{col 14}{res}{space 2} .3643994{col 26}{space 2} .2105855{col 37}{space 1}    1.73{col 46}{space 3}0.089{col 54}{space 4}-.0572908{col 67}{space 3} .7860896
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .6954998{col 26}{space 2}  .136531{col 37}{space 1}    5.09{col 46}{space 3}0.000{col 54}{space 4} .4221011{col 67}{space 3} .9688985
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 1.391152{col 26}{space 2} .2920993{col 37}{space 1}    4.76{col 46}{space 3}0.000{col 54}{space 4} .8062335{col 67}{space 3} 1.976071
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.664706{col 26}{space 2} .2515801{col 37}{space 1}   10.59{col 46}{space 3}0.000{col 54}{space 4} 2.160926{col 67}{space 3} 3.168487
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a11.doc"'"':a11.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a11.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:6}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,434}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:63}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,437.8342}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:57}
{txt}{col 51}{lalign 15:F({res:7}, {res:51})}{col 66} = {res}{ralign 10:16.42}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.1152}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}indirect {c |}{col 14}{res}{space 2}-.0726601{col 26}{space 2} .1211647{col 37}{space 1}   -0.60{col 46}{space 3}0.551{col 54}{space 4}-.3152882{col 67}{space 3}  .169968
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.1013267{col 26}{space 2} .1003896{col 37}{space 1}   -1.01{col 46}{space 3}0.317{col 54}{space 4}-.3023534{col 67}{space 3}    .0997
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}-.0828212{col 26}{space 2} .1349078{col 37}{space 1}   -0.61{col 46}{space 3}0.542{col 54}{space 4}-.3529693{col 67}{space 3}  .187327
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2}  .102199{col 26}{space 2}   .13292{col 37}{space 1}    0.77{col 46}{space 3}0.445{col 54}{space 4}-.1639687{col 67}{space 3} .3683666
{txt}{space 9}edr {c |}{col 14}{res}{space 2} -.046858{col 26}{space 2} .1823748{col 37}{space 1}   -0.26{col 46}{space 3}0.798{col 54}{space 4}-.4120572{col 67}{space 3} .3183413
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .2583117{col 26}{space 2} .1147709{col 37}{space 1}    2.25{col 46}{space 3}0.028{col 54}{space 4} .0284869{col 67}{space 3} .4881366
{txt}{space 10}m1 {c |}{col 14}{res}{space 2}  2.52811{col 26}{space 2} .2347524{col 37}{space 1}   10.77{col 46}{space 3}0.000{col 54}{space 4} 2.058026{col 67}{space 3} 2.998194
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.786078{col 26}{space 2} .1933493{col 37}{space 1}    9.24{col 46}{space 3}0.000{col 54}{space 4} 1.398903{col 67}{space 3} 2.173253
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a11.doc"'"':a11.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a11.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:3}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,390}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:81}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,400.2687}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:78}
{txt}{col 51}{lalign 15:F({res:7}, {res:72})}{col 66} = {res}{ralign 10:25.81}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.1520}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}indirect {c |}{col 14}{res}{space 2}-.1950497{col 26}{space 2}  .131439{col 37}{space 1}   -1.48{col 46}{space 3}0.142{col 54}{space 4}-.4567246{col 67}{space 3} .0666252
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.0257885{col 26}{space 2}  .090037{col 37}{space 1}   -0.29{col 46}{space 3}0.775{col 54}{space 4}-.2050384{col 67}{space 3} .1534614
{txt}{space 4}quintall {c |}{col 14}{res}{space 2} -.187461{col 26}{space 2} .1556722{col 37}{space 1}   -1.20{col 46}{space 3}0.232{col 54}{space 4}-.4973807{col 67}{space 3} .1224586
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2}-.0852751{col 26}{space 2} .1428686{col 37}{space 1}   -0.60{col 46}{space 3}0.552{col 54}{space 4}-.3697046{col 67}{space 3} .1991544
{txt}{space 9}edr {c |}{col 14}{res}{space 2} .2109054{col 26}{space 2} .2837953{col 37}{space 1}    0.74{col 46}{space 3}0.460{col 54}{space 4}-.3540875{col 67}{space 3} .7758984
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .1407781{col 26}{space 2} .1233217{col 37}{space 1}    1.14{col 46}{space 3}0.257{col 54}{space 4}-.1047365{col 67}{space 3} .3862928
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 3.079899{col 26}{space 2} .2380244{col 37}{space 1}   12.94{col 46}{space 3}0.000{col 54}{space 4} 2.606029{col 67}{space 3} 3.553769
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.355929{col 26}{space 2} .2871211{col 37}{space 1}    8.21{col 46}{space 3}0.000{col 54}{space 4} 1.784314{col 67}{space 3} 2.927543
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a11.doc"'"':a11.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a11.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 3:6}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:2,860}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 3:129}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,405.5009}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:123}
{txt}{col 51}{lalign 15:F({res:7}, {res:117})}{col 66} = {res}{ralign 10:24.62}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.1311}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}indirect {c |}{col 14}{res}{space 2}-.2050384{col 26}{space 2} .1357682{col 37}{space 1}   -1.51{col 46}{space 3}0.134{col 54}{space 4}-.4737832{col 67}{space 3} .0637065
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.0867083{col 26}{space 2} .0791175{col 37}{space 1}   -1.10{col 46}{space 3}0.275{col 54}{space 4}-.2433165{col 67}{space 3} .0698999
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}-.1363439{col 26}{space 2} .1107706{col 37}{space 1}   -1.23{col 46}{space 3}0.221{col 54}{space 4}-.3556074{col 67}{space 3} .0829196
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2}-.1643297{col 26}{space 2} .1012402{col 37}{space 1}   -1.62{col 46}{space 3}0.107{col 54}{space 4}-.3647284{col 67}{space 3} .0360689
{txt}{space 9}edr {c |}{col 14}{res}{space 2}-.0895539{col 26}{space 2} .1355671{col 37}{space 1}   -0.66{col 46}{space 3}0.510{col 54}{space 4}-.3579007{col 67}{space 3}  .178793
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .3268616{col 26}{space 2} .0909196{col 37}{space 1}    3.60{col 46}{space 3}0.000{col 54}{space 4} .1468919{col 67}{space 3} .5068314
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 2.465958{col 26}{space 2} .1901421{col 37}{space 1}   12.97{col 46}{space 3}0.000{col 54}{space 4} 2.089584{col 67}{space 3} 2.842333
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.544433{col 26}{space 2} .1862192{col 37}{space 1}   13.66{col 46}{space 3}0.000{col 54}{space 4} 2.175824{col 67}{space 3} 2.913043
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a11.doc"'"':a11.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a11.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 3:7}{txt}{col 55}{lalign 15:Number of obs}{col 70} = {res}{ralign 6:1,357}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 3:125}{txt}{col 55}{lalign 15:Population size}{col 70} = {res}{ralign 6:1,357}
{txt}{col 55}{lalign 15:Design df}{col 70} = {res}{ralign 6:118}
{txt}{col 55}{lalign 15:F({res:7}, {res:112})}{col 70} = {res}{ralign 6:16.50}
{txt}{col 55}{lalign 15:Prob > F}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 55}{lalign 15:R-squared}{col 70} = {res}{ralign 6:0.0749}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}indirect {c |}{col 14}{res}{space 2}-.2637915{col 26}{space 2} .1681887{col 37}{space 1}   -1.57{col 46}{space 3}0.119{col 54}{space 4}-.5968509{col 67}{space 3}  .069268
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.0146196{col 26}{space 2} .0790314{col 37}{space 1}   -0.18{col 46}{space 3}0.854{col 54}{space 4}-.1711232{col 67}{space 3}  .141884
{txt}{space 4}quintall {c |}{col 14}{res}{space 2} .2026868{col 26}{space 2} .1798534{col 37}{space 1}    1.13{col 46}{space 3}0.262{col 54}{space 4}-.1534719{col 67}{space 3} .5588456
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .0627852{col 26}{space 2} .1497549{col 37}{space 1}    0.42{col 46}{space 3}0.676{col 54}{space 4}-.2337701{col 67}{space 3} .3593406
{txt}{space 9}edr {c |}{col 14}{res}{space 2} .2398134{col 26}{space 2} .2170263{col 37}{space 1}    1.10{col 46}{space 3}0.271{col 54}{space 4}-.1899577{col 67}{space 3} .6695845
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .1902851{col 26}{space 2} .1642842{col 37}{space 1}    1.16{col 46}{space 3}0.249{col 54}{space 4}-.1350423{col 67}{space 3} .5156124
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 2.401028{col 26}{space 2} .2399302{col 37}{space 1}   10.01{col 46}{space 3}0.000{col 54}{space 4} 1.925901{col 67}{space 3} 2.876155
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.285844{col 26}{space 2} .2258163{col 37}{space 1}   10.12{col 46}{space 3}0.000{col 54}{space 4} 1.838666{col 67}{space 3} 2.733021
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a11.doc"'"':a11.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a11.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:6}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,350}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:63}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,347.3054}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:57}
{txt}{col 51}{lalign 15:F({res:7}, {res:51})}{col 66} = {res}{ralign 10:24.13}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.1076}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}indirect {c |}{col 14}{res}{space 2} .1244657{col 26}{space 2} .1461431{col 37}{space 1}    0.85{col 46}{space 3}0.398{col 54}{space 4}-.1681808{col 67}{space 3} .4171122
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.0784489{col 26}{space 2}  .110833{col 37}{space 1}   -0.71{col 46}{space 3}0.482{col 54}{space 4}-.3003881{col 67}{space 3} .1434903
{txt}{space 4}quintall {c |}{col 14}{res}{space 2} .0906445{col 26}{space 2}  .200418{col 37}{space 1}    0.45{col 46}{space 3}0.653{col 54}{space 4}-.3106857{col 67}{space 3} .4919747
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .4625136{col 26}{space 2}  .167924{col 37}{space 1}    2.75{col 46}{space 3}0.008{col 54}{space 4} .1262516{col 67}{space 3} .7987756
{txt}{space 9}edr {c |}{col 14}{res}{space 2} .3507099{col 26}{space 2} .2424581{col 37}{space 1}    1.45{col 46}{space 3}0.154{col 54}{space 4} -.134804{col 67}{space 3} .8362238
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .3802402{col 26}{space 2} .1101918{col 37}{space 1}    3.45{col 46}{space 3}0.001{col 54}{space 4}  .159585{col 67}{space 3} .6008954
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 2.626453{col 26}{space 2} .2096512{col 37}{space 1}   12.53{col 46}{space 3}0.000{col 54}{space 4} 2.206634{col 67}{space 3} 3.046272
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.748274{col 26}{space 2} .2291845{col 37}{space 1}    7.63{col 46}{space 3}0.000{col 54}{space 4} 1.289339{col 67}{space 3} 2.207208
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a11.doc"'"':a11.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a11.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 3:9}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,307}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 3:123}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,243.8194}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:114}
{txt}{col 51}{lalign 15:F({res:7}, {res:108})}{col 66} = {res}{ralign 10:5.83}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0766}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}indirect {c |}{col 14}{res}{space 2} .2207935{col 26}{space 2} .2919667{col 37}{space 1}    0.76{col 46}{space 3}0.451{col 54}{space 4}-.3575903{col 67}{space 3} .7991773
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.0622956{col 26}{space 2} .1124558{col 37}{space 1}   -0.55{col 46}{space 3}0.581{col 54}{space 4}-.2850697{col 67}{space 3} .1604785
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}   .21525{col 26}{space 2} .1972493{col 37}{space 1}    1.09{col 46}{space 3}0.277{col 54}{space 4}-.1754993{col 67}{space 3} .6059993
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .9610788{col 26}{space 2} .2091779{col 37}{space 1}    4.59{col 46}{space 3}0.000{col 54}{space 4} .5466989{col 67}{space 3} 1.375459
{txt}{space 9}edr {c |}{col 14}{res}{space 2} .5162473{col 26}{space 2} .2979533{col 37}{space 1}    1.73{col 46}{space 3}0.086{col 54}{space 4} -.073996{col 67}{space 3}  1.10649
{txt}{space 7}rural {c |}{col 14}{res}{space 2}-.0117027{col 26}{space 2} .1939249{col 37}{space 1}   -0.06{col 46}{space 3}0.952{col 54}{space 4}-.3958665{col 67}{space 3} .3724611
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 1.489341{col 26}{space 2} .2991776{col 37}{space 1}    4.98{col 46}{space 3}0.000{col 54}{space 4} .8966728{col 67}{space 3}  2.08201
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.962858{col 26}{space 2} .3723438{col 37}{space 1}    7.96{col 46}{space 3}0.000{col 54}{space 4} 2.225248{col 67}{space 3} 3.700468
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a11.doc"'"':a11.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a11.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:2}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,468}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:63}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,456.3492}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:61}
{txt}{col 51}{lalign 15:F({res:7}, {res:55})}{col 66} = {res}{ralign 10:36.29}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.1395}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}indirect {c |}{col 14}{res}{space 2}-.3452332{col 26}{space 2} .1479054{col 37}{space 1}   -2.33{col 46}{space 3}0.023{col 54}{space 4}-.6409883{col 67}{space 3}-.0494781
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.3086275{col 26}{space 2} .0798048{col 37}{space 1}   -3.87{col 46}{space 3}0.000{col 54}{space 4} -.468207{col 67}{space 3} -.149048
{txt}{space 4}quintall {c |}{col 14}{res}{space 2} .1590075{col 26}{space 2} .1391078{col 37}{space 1}    1.14{col 46}{space 3}0.257{col 54}{space 4}-.1191557{col 67}{space 3} .4371707
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2}  1.05333{col 26}{space 2} .1112568{col 37}{space 1}    9.47{col 46}{space 3}0.000{col 54}{space 4} .8308587{col 67}{space 3} 1.275802
{txt}{space 9}edr {c |}{col 14}{res}{space 2}  1.11119{col 26}{space 2} .1912514{col 37}{space 1}    5.81{col 46}{space 3}0.000{col 54}{space 4} .7287594{col 67}{space 3} 1.493621
{txt}{space 7}rural {c |}{col 14}{res}{space 2}-.1606665{col 26}{space 2} .1595101{col 37}{space 1}   -1.01{col 46}{space 3}0.318{col 54}{space 4}-.4796266{col 67}{space 3} .1582936
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 1.723435{col 26}{space 2} .1772674{col 37}{space 1}    9.72{col 46}{space 3}0.000{col 54}{space 4} 1.368967{col 67}{space 3} 2.077903
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.311824{col 26}{space 2}  .196244{col 37}{space 1}   16.88{col 46}{space 3}0.000{col 54}{space 4}  2.91941{col 67}{space 3} 3.704239
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a11.doc"'"':a11.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a11.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 3:5}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,469}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 3:125}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,472.1816}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:120}
{txt}{col 51}{lalign 15:F({res:7}, {res:114})}{col 66} = {res}{ralign 10:20.75}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.1139}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}indirect {c |}{col 14}{res}{space 2}-.1389253{col 26}{space 2}  .155498{col 37}{space 1}   -0.89{col 46}{space 3}0.373{col 54}{space 4}-.4468006{col 67}{space 3} .1689499
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.0576302{col 26}{space 2} .0930817{col 37}{space 1}   -0.62{col 46}{space 3}0.537{col 54}{space 4}-.2419254{col 67}{space 3} .1266651
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}-.1858485{col 26}{space 2} .1617613{col 37}{space 1}   -1.15{col 46}{space 3}0.253{col 54}{space 4}-.5061246{col 67}{space 3} .1344276
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .3483179{col 26}{space 2} .1791882{col 37}{space 1}    1.94{col 46}{space 3}0.054{col 54}{space 4}-.0064622{col 67}{space 3} .7030981
{txt}{space 9}edr {c |}{col 14}{res}{space 2}  .436672{col 26}{space 2} .2765098{col 37}{space 1}    1.58{col 46}{space 3}0.117{col 54}{space 4}-.1107982{col 67}{space 3} .9841421
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .3772585{col 26}{space 2}  .167844{col 37}{space 1}    2.25{col 46}{space 3}0.026{col 54}{space 4} .0449391{col 67}{space 3} .7095778
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 2.334296{col 26}{space 2} .2161316{col 37}{space 1}   10.80{col 46}{space 3}0.000{col 54}{space 4} 1.906371{col 67}{space 3} 2.762222
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.474814{col 26}{space 2} .2246895{col 37}{space 1}    6.56{col 46}{space 3}0.000{col 54}{space 4} 1.029944{col 67}{space 3} 1.919683
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a11.doc"'"':a11.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a11.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:8}{txt}{col 55}{lalign 15:Number of obs}{col 70} = {res}{ralign 6:1,401}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:83}{txt}{col 55}{lalign 15:Population size}{col 70} = {res}{ralign 6:1,401}
{txt}{col 55}{lalign 15:Design df}{col 70} = {res}{ralign 6:75}
{txt}{col 55}{lalign 15:F({res:7}, {res:69})}{col 70} = {res}{ralign 6:82.41}
{txt}{col 55}{lalign 15:Prob > F}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 55}{lalign 15:R-squared}{col 70} = {res}{ralign 6:0.4068}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}indirect {c |}{col 14}{res}{space 2} .0078174{col 26}{space 2} .2638793{col 37}{space 1}    0.03{col 46}{space 3}0.976{col 54}{space 4}-.5178571{col 67}{space 3} .5334919
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.1565689{col 26}{space 2}  .077032{col 37}{space 1}   -2.03{col 46}{space 3}0.046{col 54}{space 4}-.3100245{col 67}{space 3}-.0031132
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}-.0876438{col 26}{space 2} .1412302{col 37}{space 1}   -0.62{col 46}{space 3}0.537{col 54}{space 4}-.3689887{col 67}{space 3} .1937011
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .2536346{col 26}{space 2} .1675959{col 37}{space 1}    1.51{col 46}{space 3}0.134{col 54}{space 4}-.0802335{col 67}{space 3} .5875028
{txt}{space 9}edr {c |}{col 14}{res}{space 2} .1049089{col 26}{space 2} .2897737{col 37}{space 1}    0.36{col 46}{space 3}0.718{col 54}{space 4}-.4723498{col 67}{space 3} .6821676
{txt}{space 7}rural {c |}{col 14}{res}{space 2}-.1238091{col 26}{space 2} .2036607{col 37}{space 1}   -0.61{col 46}{space 3}0.545{col 54}{space 4}-.5295219{col 67}{space 3} .2819037
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 4.582489{col 26}{space 2} .1948036{col 37}{space 1}   23.52{col 46}{space 3}0.000{col 54}{space 4} 4.194421{col 67}{space 3} 4.970558
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.999847{col 26}{space 2} .2622183{col 37}{space 1}    7.63{col 46}{space 3}0.000{col 54}{space 4} 1.477481{col 67}{space 3} 2.522213
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a11.doc"'"':a11.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a11.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:7}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,359}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:84}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,348.2142}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:77}
{txt}{col 51}{lalign 15:F({res:7}, {res:71})}{col 66} = {res}{ralign 10:26.69}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.1308}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}indirect {c |}{col 14}{res}{space 2} -.179648{col 26}{space 2} .1854612{col 37}{space 1}   -0.97{col 46}{space 3}0.336{col 54}{space 4}-.5489485{col 67}{space 3} .1896525
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.1122719{col 26}{space 2} .1018033{col 37}{space 1}   -1.10{col 46}{space 3}0.274{col 54}{space 4}-.3149882{col 67}{space 3} .0904444
{txt}{space 4}quintall {c |}{col 14}{res}{space 2} .2421769{col 26}{space 2} .1620379{col 37}{space 1}    1.49{col 46}{space 3}0.139{col 54}{space 4}-.0804817{col 67}{space 3} .5648355
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2}  .868595{col 26}{space 2} .1503882{col 37}{space 1}    5.78{col 46}{space 3}0.000{col 54}{space 4} .5691337{col 67}{space 3} 1.168056
{txt}{space 9}edr {c |}{col 14}{res}{space 2} .5113712{col 26}{space 2} .2437198{col 37}{space 1}    2.10{col 46}{space 3}0.039{col 54}{space 4} .0260631{col 67}{space 3} .9966792
{txt}{space 7}rural {c |}{col 14}{res}{space 2}-.0278546{col 26}{space 2} .2101496{col 37}{space 1}   -0.13{col 46}{space 3}0.895{col 54}{space 4} -.446316{col 67}{space 3} .3906067
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 2.391646{col 26}{space 2} .1953494{col 37}{space 1}   12.24{col 46}{space 3}0.000{col 54}{space 4} 2.002655{col 67}{space 3} 2.780636
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.549402{col 26}{space 2} .2047784{col 37}{space 1}   12.45{col 46}{space 3}0.000{col 54}{space 4} 2.141637{col 67}{space 3} 2.957168
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a11.doc"'"':a11.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a11.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:4}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,467}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:63}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,447.6974}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:59}
{txt}{col 51}{lalign 15:F({res:7}, {res:53})}{col 66} = {res}{ralign 10:19.50}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0793}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}indirect {c |}{col 14}{res}{space 2}-.4935831{col 26}{space 2} .1020509{col 37}{space 1}   -4.84{col 46}{space 3}0.000{col 54}{space 4}-.6977864{col 67}{space 3}-.2893798
{txt}{space 7}woman {c |}{col 14}{res}{space 2} .0339343{col 26}{space 2} .0893148{col 37}{space 1}    0.38{col 46}{space 3}0.705{col 54}{space 4}-.1447841{col 67}{space 3} .2126528
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}-.0528828{col 26}{space 2} .1630186{col 37}{space 1}   -0.32{col 46}{space 3}0.747{col 54}{space 4}-.3790823{col 67}{space 3} .2733166
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2}-.4458142{col 26}{space 2} .1944265{col 37}{space 1}   -2.29{col 46}{space 3}0.025{col 54}{space 4}-.8348607{col 67}{space 3}-.0567678
{txt}{space 9}edr {c |}{col 14}{res}{space 2}-.1543626{col 26}{space 2} .2350906{col 37}{space 1}   -0.66{col 46}{space 3}0.514{col 54}{space 4}-.6247778{col 67}{space 3} .3160526
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .1566301{col 26}{space 2} .0990846{col 37}{space 1}    1.58{col 46}{space 3}0.119{col 54}{space 4}-.0416378{col 67}{space 3}  .354898
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 2.306562{col 26}{space 2} .2522438{col 37}{space 1}    9.14{col 46}{space 3}0.000{col 54}{space 4} 1.801823{col 67}{space 3} 2.811301
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  2.24611{col 26}{space 2} .2914651{col 37}{space 1}    7.71{col 46}{space 3}0.000{col 54}{space 4}  1.66289{col 67}{space 3} 2.829331
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a11.doc"'"':a11.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a11.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:5}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,176}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:62}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,166.6666}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:57}
{txt}{col 51}{lalign 15:F({res:7}, {res:51})}{col 66} = {res}{ralign 10:6.20}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0553}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}indirect {c |}{col 14}{res}{space 2} .4932468{col 26}{space 2} .1316106{col 37}{space 1}    3.75{col 46}{space 3}0.000{col 54}{space 4}  .229701{col 67}{space 3} .7567925
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.1120014{col 26}{space 2}  .086118{col 37}{space 1}   -1.30{col 46}{space 3}0.199{col 54}{space 4}-.2844497{col 67}{space 3}  .060447
{txt}{space 4}quintall {c |}{col 14}{res}{space 2} .4494859{col 26}{space 2} .1711858{col 37}{space 1}    2.63{col 46}{space 3}0.011{col 54}{space 4} .1066922{col 67}{space 3} .7922795
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2}-.1525712{col 26}{space 2}  .181472{col 37}{space 1}   -0.84{col 46}{space 3}0.404{col 54}{space 4}-.5159627{col 67}{space 3} .2108203
{txt}{space 9}edr {c |}{col 14}{res}{space 2}-.7169041{col 26}{space 2} .2996805{col 37}{space 1}   -2.39{col 46}{space 3}0.020{col 54}{space 4}-1.317004{col 67}{space 3}-.1168043
{txt}{space 7}rural {c |}{col 14}{res}{space 2}-.3083595{col 26}{space 2} .1176775{col 37}{space 1}   -2.62{col 46}{space 3}0.011{col 54}{space 4}-.5440046{col 67}{space 3}-.0727144
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} .8830042{col 26}{space 2} .2115926{col 37}{space 1}    4.17{col 46}{space 3}0.000{col 54}{space 4} .4592974{col 67}{space 3} 1.306711
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.572312{col 26}{space 2} .2492536{col 37}{space 1}   10.32{col 46}{space 3}0.000{col 54}{space 4}  2.07319{col 67}{space 3} 3.071434
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a11.doc"'"':a11.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a11.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:4}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,375}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:58}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,372.2555}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:54}
{txt}{col 51}{lalign 15:F({res:7}, {res:48})}{col 66} = {res}{ralign 10:16.68}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0941}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}indirect {c |}{col 14}{res}{space 2}-.2444325{col 26}{space 2} .1280964{col 37}{space 1}   -1.91{col 46}{space 3}0.062{col 54}{space 4}-.5012503{col 67}{space 3} .0123852
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.0198623{col 26}{space 2} .0938995{col 37}{space 1}   -0.21{col 46}{space 3}0.833{col 54}{space 4}-.2081195{col 67}{space 3}  .168395
{txt}{space 4}quintall {c |}{col 14}{res}{space 2} .0709505{col 26}{space 2} .1561431{col 37}{space 1}    0.45{col 46}{space 3}0.651{col 54}{space 4}-.2420977{col 67}{space 3} .3839986
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .4211136{col 26}{space 2} .1501081{col 37}{space 1}    2.81{col 46}{space 3}0.007{col 54}{space 4} .1201651{col 67}{space 3} .7220622
{txt}{space 9}edr {c |}{col 14}{res}{space 2}-.0274085{col 26}{space 2} .2633147{col 37}{space 1}   -0.10{col 46}{space 3}0.917{col 54}{space 4}-.5553226{col 67}{space 3} .5005057
{txt}{space 7}rural {c |}{col 14}{res}{space 2}-.0068388{col 26}{space 2} .1036486{col 37}{space 1}   -0.07{col 46}{space 3}0.948{col 54}{space 4}-.2146417{col 67}{space 3} .2009642
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 1.794498{col 26}{space 2} .2153131{col 37}{space 1}    8.33{col 46}{space 3}0.000{col 54}{space 4} 1.362821{col 67}{space 3} 2.226175
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.250006{col 26}{space 2} .2299058{col 37}{space 1}    9.79{col 46}{space 3}0.000{col 54}{space 4} 1.789073{col 67}{space 3} 2.710939
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a11.doc"'"':a11.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a11.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:4}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,430}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:51}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,377.6493}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:47}
{txt}{col 51}{lalign 15:F({res:7}, {res:41})}{col 66} = {res}{ralign 10:22.55}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.1652}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}indirect {c |}{col 14}{res}{space 2}  .121151{col 26}{space 2}  .188557{col 37}{space 1}    0.64{col 46}{space 3}0.524{col 54}{space 4}-.2581768{col 67}{space 3} .5004788
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.1774176{col 26}{space 2} .0750455{col 37}{space 1}   -2.36{col 46}{space 3}0.022{col 54}{space 4}-.3283897{col 67}{space 3}-.0264455
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}-.2066699{col 26}{space 2} .1567989{col 37}{space 1}   -1.32{col 46}{space 3}0.194{col 54}{space 4}-.5221085{col 67}{space 3} .1087687
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .0190406{col 26}{space 2} .1630069{col 37}{space 1}    0.12{col 46}{space 3}0.908{col 54}{space 4} -.308887{col 67}{space 3} .3469683
{txt}{space 9}edr {c |}{col 14}{res}{space 2}-.5195319{col 26}{space 2} .2923317{col 37}{space 1}   -1.78{col 46}{space 3}0.082{col 54}{space 4}-1.107628{col 67}{space 3} .0685637
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .2737182{col 26}{space 2} .1738366{col 37}{space 1}    1.57{col 46}{space 3}0.122{col 54}{space 4}-.0759959{col 67}{space 3} .6234323
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 2.766332{col 26}{space 2} .2143902{col 37}{space 1}   12.90{col 46}{space 3}0.000{col 54}{space 4} 2.335035{col 67}{space 3}  3.19763
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  2.40021{col 26}{space 2} .2423497{col 37}{space 1}    9.90{col 46}{space 3}0.000{col 54}{space 4} 1.912666{col 67}{space 3} 2.887755
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a11.doc"'"':a11.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a11.txt""':seeout}
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 2:6}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,449}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 2:63}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,417.8083}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:57}
{txt}{col 51}{lalign 15:F({res:7}, {res:51})}{col 66} = {res}{ralign 10:13.62}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0898}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}indirect {c |}{col 14}{res}{space 2}-.1247149{col 26}{space 2} .1144304{col 37}{space 1}   -1.09{col 46}{space 3}0.280{col 54}{space 4}-.3538578{col 67}{space 3}  .104428
{txt}{space 7}woman {c |}{col 14}{res}{space 2}-.1412549{col 26}{space 2} .0775562{col 37}{space 1}   -1.82{col 46}{space 3}0.074{col 54}{space 4}-.2965585{col 67}{space 3} .0140487
{txt}{space 4}quintall {c |}{col 14}{res}{space 2}-.3778901{col 26}{space 2} .1404385{col 37}{space 1}   -2.69{col 46}{space 3}0.009{col 54}{space 4}-.6591135{col 67}{space 3}-.0966668
{txt}{space 3}agecohort {c |}{col 14}{res}{space 2} .4607856{col 26}{space 2} .1714169{col 37}{space 1}    2.69{col 46}{space 3}0.009{col 54}{space 4} .1175292{col 67}{space 3} .8040419
{txt}{space 9}edr {c |}{col 14}{res}{space 2} .1115561{col 26}{space 2} .2440406{col 37}{space 1}    0.46{col 46}{space 3}0.649{col 54}{space 4}-.3771267{col 67}{space 3}  .600239
{txt}{space 7}rural {c |}{col 14}{res}{space 2} .2691644{col 26}{space 2} .1058427{col 37}{space 1}    2.54{col 46}{space 3}0.014{col 54}{space 4}  .057218{col 67}{space 3} .4811108
{txt}{space 10}m1 {c |}{col 14}{res}{space 2} 2.067061{col 26}{space 2} .2333723{col 37}{space 1}    8.86{col 46}{space 3}0.000{col 54}{space 4} 1.599741{col 67}{space 3} 2.534381
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.605668{col 26}{space 2} .2153556{col 37}{space 1}   12.10{col 46}{space 3}0.000{col 54}{space 4} 2.174426{col 67}{space 3}  3.03691
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{stata `"shellout using `"a11.doc"'"':a11.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a11.txt""':seeout}

{com}. 
. ***A12: months by country
. tab pais months if wave==2014

                      {txt}{c |}                                                               months
              Country {c |}         1          4          5          9         11         18         22         26         27         31         35         41 {c |}     Total
{hline 22}{c +}{hline 132}{c +}{hline 10}
               Mexico {c |}{res}         0          0          0          0          0      1,535          0          0          0          0          0          0 {txt}{c |}{res}     1,535 
{txt}            Guatemala {c |}{res}         0          0          0          0          0          0          0          0          0      1,506          0          0 {txt}{c |}{res}     1,506 
{txt}          El Salvador {c |}{res}     1,512          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}     1,512 
{txt}             Honduras {c |}{res}         0      1,561          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}     1,561 
{txt}            Nicaragua {c |}{res}         0          0          0          0          0          0          0          0      1,546          0          0          0 {txt}{c |}{res}     1,546 
{txt}           Costa Rica {c |}{res}     1,537          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}     1,537 
{txt}               Panama {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}     1,508 
{txt}             Colombia {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}     1,496 
{txt}              Ecuador {c |}{res}         0          0          0          0      1,489          0          0          0          0          0          0          0 {txt}{c |}{res}     1,489 
{txt}              Bolivia {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}     3,066 
{txt}                 Peru {c |}{res}         0          0          0          0          0          0          0          0          0      1,500          0          0 {txt}{c |}{res}     1,500 
{txt}             Paraguay {c |}{res}         0          0          0      1,503          0          0          0          0          0          0          0          0 {txt}{c |}{res}     1,503 
{txt}                Chile {c |}{res}         0      1,571          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}     1,571 
{txt}              Uruguay {c |}{res}         0          0          0          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}     1,512 
{txt}               Brazil {c |}{res}         0          0          0          0          0          0          0          0          0          0          0      1,500 {txt}{c |}{res}     1,500 
{txt}            Venezuela {c |}{res}         0          0          0          0      1,500          0          0          0          0          0          0          0 {txt}{c |}{res}     1,500 
{txt}            Argentina {c |}{res}         0          0      1,512          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}     1,512 
{txt}   Dominican Republic {c |}{res}         0          0          0          0          0          0      1,520          0          0          0          0          0 {txt}{c |}{res}     1,520 
{txt}                Haiti {c |}{res}         0          0          0          0          0          0          0          0          0          0      1,512          0 {txt}{c |}{res}     1,512 
{txt}              Jamaica {c |}{res}         0          0          0          0          0          0          0      1,503          0          0          0          0 {txt}{c |}{res}     1,503 
{txt}               Guyana {c |}{res}         0          0          0          0          0          0          0          0          0      1,557          0          0 {txt}{c |}{res}     1,557 
{txt}               Belize {c |}{res}         0          0          0          0          0          0          0      1,533          0          0          0          0 {txt}{c |}{res}     1,533 
{txt}{hline 22}{c +}{hline 132}{c +}{hline 10}
                Total {c |}{res}     3,049      3,132      1,512      1,503      2,989      1,535      1,520      3,036      1,546      4,563      1,512      1,500 {txt}{c |}{res}    34,979 


                      {txt}{c |}              months
              Country {c |}        46         52         58 {c |}     Total
{hline 22}{c +}{hline 33}{c +}{hline 10}
               Mexico {c |}{res}         0          0          0 {txt}{c |}{res}     1,535 
{txt}            Guatemala {c |}{res}         0          0          0 {txt}{c |}{res}     1,506 
{txt}          El Salvador {c |}{res}         0          0          0 {txt}{c |}{res}     1,512 
{txt}             Honduras {c |}{res}         0          0          0 {txt}{c |}{res}     1,561 
{txt}            Nicaragua {c |}{res}         0          0          0 {txt}{c |}{res}     1,546 
{txt}           Costa Rica {c |}{res}         0          0          0 {txt}{c |}{res}     1,537 
{txt}               Panama {c |}{res}         0          0      1,508 {txt}{c |}{res}     1,508 
{txt}             Colombia {c |}{res}     1,496          0          0 {txt}{c |}{res}     1,496 
{txt}              Ecuador {c |}{res}         0          0          0 {txt}{c |}{res}     1,489 
{txt}              Bolivia {c |}{res}         0      3,066          0 {txt}{c |}{res}     3,066 
{txt}                 Peru {c |}{res}         0          0          0 {txt}{c |}{res}     1,500 
{txt}             Paraguay {c |}{res}         0          0          0 {txt}{c |}{res}     1,503 
{txt}                Chile {c |}{res}         0          0          0 {txt}{c |}{res}     1,571 
{txt}              Uruguay {c |}{res}         0      1,512          0 {txt}{c |}{res}     1,512 
{txt}               Brazil {c |}{res}         0          0          0 {txt}{c |}{res}     1,500 
{txt}            Venezuela {c |}{res}         0          0          0 {txt}{c |}{res}     1,500 
{txt}            Argentina {c |}{res}         0          0          0 {txt}{c |}{res}     1,512 
{txt}   Dominican Republic {c |}{res}         0          0          0 {txt}{c |}{res}     1,520 
{txt}                Haiti {c |}{res}         0          0          0 {txt}{c |}{res}     1,512 
{txt}              Jamaica {c |}{res}         0          0          0 {txt}{c |}{res}     1,503 
{txt}               Guyana {c |}{res}         0          0          0 {txt}{c |}{res}     1,557 
{txt}               Belize {c |}{res}         0          0          0 {txt}{c |}{res}     1,533 
{txt}{hline 22}{c +}{hline 33}{c +}{hline 10}
                Total {c |}{res}     1,496      4,578      1,508 {txt}{c |}{res}    34,979 
{txt}
{com}. 
. 
. ***A13: moderation by months
. mixed trustel i.indirect##c.months woman quintall agecohort edr rural m1 if wave==2014 [pweight = weight1500] || pais: 
{res}
{txt}Obtaining starting values by EM ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-60573.492}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-60573.492}  (backed up)
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 53}Number of obs{col 69} = {res} 32,507
{txt}Group variable: {res}pais{col 53}{txt}Number of groups{col 69} = {res}     22
{txt}{col 53}Obs per group:
{col 66}min = {res}  1,176
{txt}{col 66}avg = {res}1,477.6
{txt}{col 66}max = {res}  2,860
{col 53}{txt}Wald chi2({res}9{txt}){col 69} = {res} 424.08
{txt}Log pseudolikelihood = {res}-60573.492{col 53}{txt}Prob > chi2{col 69} = {res} 0.0000

{txt}{ralign 83:(Std. err. adjusted for {res:22} clusters in {res:pais})}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}          trustel{col 19}{c |} Coefficient{col 31}  std. err.{col 43}      z{col 51}   P>|z|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}1.indirect {c |}{col 19}{res}{space 2}-.2536426{col 31}{space 2}  .109035{col 42}{space 1}   -2.33{col 51}{space 3}0.020{col 59}{space 4}-.4673474{col 72}{space 3}-.0399379
{txt}{space 11}months {c |}{col 19}{res}{space 2}-.0095292{col 31}{space 2} .0099196{col 42}{space 1}   -0.96{col 51}{space 3}0.337{col 59}{space 4}-.0289712{col 72}{space 3} .0099128
{txt}{space 17} {c |}
indirect#c.months {c |}
{space 15}1  {c |}{col 19}{res}{space 2} .0041385{col 31}{space 2} .0034971{col 42}{space 1}    1.18{col 51}{space 3}0.237{col 59}{space 4}-.0027156{col 72}{space 3} .0109926
{txt}{space 17} {c |}
{space 12}woman {c |}{col 19}{res}{space 2}-.0663134{col 31}{space 2} .0177759{col 42}{space 1}   -3.73{col 51}{space 3}0.000{col 59}{space 4}-.1011536{col 72}{space 3}-.0314733
{txt}{space 9}quintall {c |}{col 19}{res}{space 2}-.0327546{col 31}{space 2} .0511968{col 42}{space 1}   -0.64{col 51}{space 3}0.522{col 59}{space 4}-.1330985{col 72}{space 3} .0675893
{txt}{space 8}agecohort {c |}{col 19}{res}{space 2} .2833796{col 31}{space 2} .0908203{col 42}{space 1}    3.12{col 51}{space 3}0.002{col 59}{space 4} .1053751{col 72}{space 3} .4613841
{txt}{space 14}edr {c |}{col 19}{res}{space 2} .1204438{col 31}{space 2} .0948733{col 42}{space 1}    1.27{col 51}{space 3}0.204{col 59}{space 4}-.0655045{col 72}{space 3} .3063921
{txt}{space 12}rural {c |}{col 19}{res}{space 2}  .180804{col 31}{space 2} .0506173{col 42}{space 1}    3.57{col 51}{space 3}0.000{col 59}{space 4} .0815958{col 72}{space 3} .2800121
{txt}{space 15}m1 {c |}{col 19}{res}{space 2} 2.584255{col 31}{space 2} .2275902{col 42}{space 1}   11.35{col 51}{space 3}0.000{col 59}{space 4} 2.138187{col 72}{space 3} 3.030324
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}  2.45335{col 31}{space 2} .2518197{col 42}{space 1}    9.74{col 51}{space 3}0.000{col 59}{space 4} 1.959792{col 72}{space 3} 2.946908
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}pais{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33} .3964438{col 44}   .15365{col 58} .1854731{col 70} .8473881
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 3.029327{col 44} .1017811{col 58} 2.836266{col 70} 3.235529
{txt}{hline 29}{c BT}{hline 48}

{p 0 9 2}Warning: Sampling weights were 
specified only at the first level 
in a multilevel model. If these weights are 
indicative of overall and not conditional 
inclusion probabilities, then 
{help mixed##sampling:results may be biased}.
{p_end}

{com}.                         outreg2 using a13.doc, dec(2) replace ctitle(Indirect)
{txt}{stata `"shellout using `"a13.doc"'"':a13.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a13.txt""':seeout}

{com}.                         margins, dydx(indirect) at(months=(0(15)60))
{res}
{txt}{col 1}Average marginal effects{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:32,507}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.indirect}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 6:months} = {res:{ralign 2:0}}
{lalign 7:2._at: }{space 0}{lalign 6:months} = {res:{ralign 2:15}}
{lalign 7:3._at: }{space 0}{lalign 6:months} = {res:{ralign 2:30}}
{lalign 7:4._at: }{space 0}{lalign 6:months} = {res:{ralign 2:45}}
{lalign 7:5._at: }{space 0}{lalign 6:months} = {res:{ralign 2:60}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.indirect  {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.indirect   {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.2536426{col 26}{space 2}  .109035{col 37}{space 1}   -2.33{col 46}{space 3}0.020{col 54}{space 4}-.4673474{col 67}{space 3}-.0399379
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.1915655{col 26}{space 2}  .076151{col 37}{space 1}   -2.52{col 46}{space 3}0.012{col 54}{space 4}-.3408187{col 67}{space 3}-.0423123
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1294883{col 26}{space 2} .0721981{col 37}{space 1}   -1.79{col 46}{space 3}0.073{col 54}{space 4} -.270994{col 67}{space 3} .0120174
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0674111{col 26}{space 2}  .100645{col 37}{space 1}   -0.67{col 46}{space 3}0.503{col 54}{space 4}-.2646718{col 67}{space 3} .1298495
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.0053339{col 26}{space 2} .1433511{col 37}{space 1}   -0.04{col 46}{space 3}0.970{col 54}{space 4} -.286297{col 67}{space 3} .2756291
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.                                 marginsplot, xtitle(Months After Election) ///
>                                         ytitle("Predicted Electoral Trust 2014, indirect") ///
>                                         title("Indirect Exposure to Vote Buying")
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:months}{p_end}
{res}{txt}
{com}.                                 graph save Graph "months_inter2014_dydx.gph", replace
{res}{txt}file {bf:months_inter2014_dydx.gph} saved

{com}. mixed trustel i.direct##c.months woman quintall agecohort edr rural m1 if wave==2014 [pweight = weight1500] || pais: 
{res}
{txt}Obtaining starting values by EM ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-61016.944}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-61016.944}  (backed up)
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 53}Number of obs{col 69} = {res} 32,745
{txt}Group variable: {res}pais{col 53}{txt}Number of groups{col 69} = {res}     22
{txt}{col 53}Obs per group:
{col 66}min = {res}  1,285
{txt}{col 66}avg = {res}1,488.4
{txt}{col 66}max = {res}  2,862
{col 53}{txt}Wald chi2({res}9{txt}){col 69} = {res} 444.52
{txt}Log pseudolikelihood = {res}-61016.944{col 53}{txt}Prob > chi2{col 69} = {res} 0.0000

{txt}{ralign 81:(Std. err. adjusted for {res:22} clusters in {res:pais})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}        trustel{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}1.direct {c |}{col 17}{res}{space 2}-.2868435{col 29}{space 2} .1138748{col 40}{space 1}   -2.52{col 49}{space 3}0.012{col 57}{space 4}-.5100341{col 70}{space 3} -.063653
{txt}{space 9}months {c |}{col 17}{res}{space 2}-.0095127{col 29}{space 2} .0098759{col 40}{space 1}   -0.96{col 49}{space 3}0.335{col 57}{space 4} -.028869{col 70}{space 3} .0098437
{txt}{space 15} {c |}
direct#c.months {c |}
{space 13}1  {c |}{col 17}{res}{space 2} .0053369{col 29}{space 2} .0033918{col 40}{space 1}    1.57{col 49}{space 3}0.116{col 57}{space 4}-.0013108{col 70}{space 3} .0119847
{txt}{space 15} {c |}
{space 10}woman {c |}{col 17}{res}{space 2}-.0649133{col 29}{space 2} .0181001{col 40}{space 1}   -3.59{col 49}{space 3}0.000{col 57}{space 4}-.1003888{col 70}{space 3}-.0294378
{txt}{space 7}quintall {c |}{col 17}{res}{space 2} -.025685{col 29}{space 2} .0512779{col 40}{space 1}   -0.50{col 49}{space 3}0.616{col 57}{space 4}-.1261879{col 70}{space 3} .0748179
{txt}{space 6}agecohort {c |}{col 17}{res}{space 2} .2847846{col 29}{space 2} .0898584{col 40}{space 1}    3.17{col 49}{space 3}0.002{col 57}{space 4} .1086654{col 70}{space 3} .4609038
{txt}{space 12}edr {c |}{col 17}{res}{space 2}  .111105{col 29}{space 2} .0930173{col 40}{space 1}    1.19{col 49}{space 3}0.232{col 57}{space 4}-.0712055{col 70}{space 3} .2934155
{txt}{space 10}rural {c |}{col 17}{res}{space 2} .1792834{col 29}{space 2} .0496487{col 40}{space 1}    3.61{col 49}{space 3}0.000{col 57}{space 4} .0819738{col 70}{space 3} .2765931
{txt}{space 13}m1 {c |}{col 17}{res}{space 2} 2.581225{col 29}{space 2} .2280299{col 40}{space 1}   11.32{col 49}{space 3}0.000{col 57}{space 4} 2.134294{col 70}{space 3} 3.028155
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 2.446814{col 29}{space 2} .2532359{col 40}{space 1}    9.66{col 49}{space 3}0.000{col 57}{space 4}  1.95048{col 70}{space 3} 2.943147
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}pais{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33} .4007005{col 44} .1547172{col 58} .1879995{col 70} .8540495
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33}  3.02695{col 44} .1010846{col 58} 2.835173{col 70}   3.2317
{txt}{hline 29}{c BT}{hline 48}

{p 0 9 2}Warning: Sampling weights were 
specified only at the first level 
in a multilevel model. If these weights are 
indicative of overall and not conditional 
inclusion probabilities, then 
{help mixed##sampling:results may be biased}.
{p_end}

{com}.                         outreg2 using a13.doc, dec(2) append ctitle(Direct)
{txt}{stata `"shellout using `"a13.doc"'"':a13.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a13.txt""':seeout}

{com}.                         margins, dydx(direct) at(months=(0(15)60))
{res}
{txt}{col 1}Average marginal effects{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:32,745}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.direct}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 6:months} = {res:{ralign 2:0}}
{lalign 7:2._at: }{space 0}{lalign 6:months} = {res:{ralign 2:15}}
{lalign 7:3._at: }{space 0}{lalign 6:months} = {res:{ralign 2:30}}
{lalign 7:4._at: }{space 0}{lalign 6:months} = {res:{ralign 2:45}}
{lalign 7:5._at: }{space 0}{lalign 6:months} = {res:{ralign 2:60}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.direct    {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.direct     {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.2868435{col 26}{space 2} .1138748{col 37}{space 1}   -2.52{col 46}{space 3}0.012{col 54}{space 4}-.5100341{col 67}{space 3} -.063653
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.2067895{col 26}{space 2} .0893751{col 37}{space 1}   -2.31{col 46}{space 3}0.021{col 54}{space 4}-.3819616{col 67}{space 3}-.0316175
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1267356{col 26}{space 2} .0904721{col 37}{space 1}   -1.40{col 46}{space 3}0.161{col 54}{space 4}-.3040576{col 67}{space 3} .0505865
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0466816{col 26}{space 2} .1164446{col 37}{space 1}   -0.40{col 46}{space 3}0.689{col 54}{space 4}-.2749088{col 67}{space 3} .1815457
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0333724{col 26}{space 2} .1552751{col 37}{space 1}    0.21{col 46}{space 3}0.830{col 54}{space 4}-.2709611{col 67}{space 3}  .337706
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.                                 marginsplot, xtitle(Months After Election) ///
>                                         ytitle("Effects on Trust 2014, direct") yline(0)  ///
>                                         title("Indirect Exposure to Vote Buying")
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:months}{p_end}
{res}{txt}
{com}.                                 graph save Graph "months_inter2014_direct_dydx.gph", replace                            
{res}{txt}file {bf:months_inter2014_direct_dydx.gph} saved

{com}. graph combine months_inter2014_direct_dydx.gph months_inter2014_dydx.gph
{res}{txt}
{com}.         graph save Graph "a13.gph", replace
{res}{txt}file {bf:a13.gph} saved

{com}. 
. 
. ***A14: moderation by electoral violence
. mixed trustel i.indirect##c.elviolence woman quintall agecohort edr rural m1 if wave==2014 [pweight = weight1500] || pais: 
{res}
{txt}Obtaining starting values by EM ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-57689.034}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-57689.034}  (backed up)
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 53}Number of obs{col 69} = {res} 31,058
{txt}Group variable: {res}pais{col 53}{txt}Number of groups{col 69} = {res}     21
{txt}{col 53}Obs per group:
{col 66}min = {res}  1,176
{txt}{col 66}avg = {res}1,479.0
{txt}{col 66}max = {res}  2,860
{col 53}{txt}Wald chi2({res}9{txt}){col 69} = {res} 787.04
{txt}Log pseudolikelihood = {res}-57689.034{col 53}{txt}Prob > chi2{col 69} = {res} 0.0000

{txt}{ralign 87:(Std. err. adjusted for {res:21} clusters in {res:pais})}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}              trustel{col 23}{c |} Coefficient{col 35}  std. err.{col 47}      z{col 55}   P>|z|{col 63}     [95% con{col 76}f. interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}1.indirect {c |}{col 23}{res}{space 2}-.2284708{col 35}{space 2}  .141732{col 46}{space 1}   -1.61{col 55}{space 3}0.107{col 63}{space 4}-.5062605{col 76}{space 3} .0493189
{txt}{space 11}elviolence {c |}{col 23}{res}{space 2} -1.61541{col 35}{space 2} .2978014{col 46}{space 1}   -5.42{col 55}{space 3}0.000{col 63}{space 4} -2.19909{col 76}{space 3} -1.03173
{txt}{space 21} {c |}
indirect#c.elviolence {c |}
{space 19}1  {c |}{col 23}{res}{space 2} .1175825{col 35}{space 2} .2838046{col 46}{space 1}    0.41{col 55}{space 3}0.679{col 63}{space 4}-.4386643{col 76}{space 3} .6738293
{txt}{space 21} {c |}
{space 16}woman {c |}{col 23}{res}{space 2}-.0629179{col 35}{space 2}  .018024{col 46}{space 1}   -3.49{col 55}{space 3}0.000{col 63}{space 4}-.0982442{col 76}{space 3}-.0275915
{txt}{space 13}quintall {c |}{col 23}{res}{space 2}-.0141029{col 35}{space 2} .0498332{col 46}{space 1}   -0.28{col 55}{space 3}0.777{col 63}{space 4}-.1117741{col 76}{space 3} .0835683
{txt}{space 12}agecohort {c |}{col 23}{res}{space 2} .2728536{col 35}{space 2} .0939981{col 46}{space 1}    2.90{col 55}{space 3}0.004{col 63}{space 4} .0886207{col 76}{space 3} .4570865
{txt}{space 18}edr {c |}{col 23}{res}{space 2} .1153527{col 35}{space 2} .0996328{col 46}{space 1}    1.16{col 55}{space 3}0.247{col 63}{space 4}-.0799239{col 76}{space 3} .3106294
{txt}{space 16}rural {c |}{col 23}{res}{space 2} .1746043{col 35}{space 2} .0537463{col 46}{space 1}    3.25{col 55}{space 3}0.001{col 63}{space 4} .0692636{col 76}{space 3} .2799451
{txt}{space 19}m1 {c |}{col 23}{res}{space 2} 2.615892{col 35}{space 2} .2351434{col 46}{space 1}   11.12{col 55}{space 3}0.000{col 63}{space 4}  2.15502{col 76}{space 3} 3.076765
{txt}{space 16}_cons {c |}{col 23}{res}{space 2} 3.073421{col 35}{space 2} .2064107{col 46}{space 1}   14.89{col 55}{space 3}0.000{col 63}{space 4} 2.668863{col 76}{space 3} 3.477978
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}pais{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33} .1710997{col 44} .0450106{col 58} .1021708{col 70} .2865312
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 3.013697{col 44} .1055343{col 58} 2.813792{col 70} 3.227804
{txt}{hline 29}{c BT}{hline 48}

{p 0 9 2}Warning: Sampling weights were 
specified only at the first level 
in a multilevel model. If these weights are 
indicative of overall and not conditional 
inclusion probabilities, then 
{help mixed##sampling:results may be biased}.
{p_end}

{com}.                         outreg2 using a14.doc, dec(2) replace ctitle(Indirect)
{txt}{stata `"shellout using `"a14.doc"'"':a14.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a14.txt""':seeout}

{com}.         margins, dydx(indirect) at(elviolence=(0(.25)1))
{res}
{txt}{col 1}Average marginal effects{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:31,058}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.indirect}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 10:elviolence} = {res:{ralign 3:0}}
{lalign 7:2._at: }{space 0}{lalign 10:elviolence} = {res:{ralign 3:.25}}
{lalign 7:3._at: }{space 0}{lalign 10:elviolence} = {res:{ralign 3:.5}}
{lalign 7:4._at: }{space 0}{lalign 10:elviolence} = {res:{ralign 3:.75}}
{lalign 7:5._at: }{space 0}{lalign 10:elviolence} = {res:{ralign 3:1}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.indirect  {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.indirect   {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.2284708{col 26}{space 2}  .141732{col 37}{space 1}   -1.61{col 46}{space 3}0.107{col 54}{space 4}-.5062605{col 67}{space 3} .0493189
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.1990752{col 26}{space 2} .0823314{col 37}{space 1}   -2.42{col 46}{space 3}0.016{col 54}{space 4}-.3604418{col 67}{space 3}-.0377085
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1696795{col 26}{space 2} .0594735{col 37}{space 1}   -2.85{col 46}{space 3}0.004{col 54}{space 4}-.2862453{col 67}{space 3}-.0531137
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.1402839{col 26}{space 2}  .101803{col 37}{space 1}   -1.38{col 46}{space 3}0.168{col 54}{space 4}-.3398141{col 67}{space 3} .0592463
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.1108883{col 26}{space 2} .1651022{col 37}{space 1}   -0.67{col 46}{space 3}0.502{col 54}{space 4}-.4344826{col 67}{space 3} .2127061
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, xtitle(Electoral Violence) ///
>                 ytitle(Effects on Electoral Trust) ///
>                 title(Indirect Exposure to Vote Buying) yline(0)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:elviolence}{p_end}
{res}{txt}
{com}.         graph save Graph "elviolence_indirect.gph", replace                             
{res}{txt}file {bf:elviolence_indirect.gph} saved

{com}. mixed trustel i.direct##c.elviolence woman quintall agecohort edr rural m1 if wave==2014 [pweight = weight1500] || pais: 
{res}
{txt}Obtaining starting values by EM ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-58097.614}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-58097.614}  (backed up)
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 53}Number of obs{col 69} = {res} 31,280
{txt}Group variable: {res}pais{col 53}{txt}Number of groups{col 69} = {res}     21
{txt}{col 53}Obs per group:
{col 66}min = {res}  1,285
{txt}{col 66}avg = {res}1,489.5
{txt}{col 66}max = {res}  2,862
{col 53}{txt}Wald chi2({res}9{txt}){col 69} = {res} 917.56
{txt}Log pseudolikelihood = {res}-58097.614{col 53}{txt}Prob > chi2{col 69} = {res} 0.0000

{txt}{ralign 85:(Std. err. adjusted for {res:21} clusters in {res:pais})}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}            trustel{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      z{col 53}   P>|z|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}1.direct {c |}{col 21}{res}{space 2}-.3113669{col 33}{space 2} .1600079{col 44}{space 1}   -1.95{col 53}{space 3}0.052{col 61}{space 4}-.6249767{col 74}{space 3} .0022429
{txt}{space 9}elviolence {c |}{col 21}{res}{space 2}-1.623897{col 33}{space 2} .2852302{col 44}{space 1}   -5.69{col 53}{space 3}0.000{col 61}{space 4}-2.182938{col 74}{space 3}-1.064856
{txt}{space 19} {c |}
direct#c.elviolence {c |}
{space 17}1  {c |}{col 21}{res}{space 2} .2455753{col 33}{space 2} .2835105{col 44}{space 1}    0.87{col 53}{space 3}0.386{col 61}{space 4} -.310095{col 74}{space 3} .8012457
{txt}{space 19} {c |}
{space 14}woman {c |}{col 21}{res}{space 2} -.061453{col 33}{space 2} .0183695{col 44}{space 1}   -3.35{col 53}{space 3}0.001{col 61}{space 4}-.0974566{col 74}{space 3}-.0254495
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}-.0080656{col 33}{space 2} .0497063{col 44}{space 1}   -0.16{col 53}{space 3}0.871{col 61}{space 4}-.1054882{col 74}{space 3}  .089357
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2} .2746333{col 33}{space 2} .0930966{col 44}{space 1}    2.95{col 53}{space 3}0.003{col 61}{space 4} .0921672{col 74}{space 3} .4570994
{txt}{space 16}edr {c |}{col 21}{res}{space 2} .1049872{col 33}{space 2} .0973092{col 44}{space 1}    1.08{col 53}{space 3}0.281{col 61}{space 4}-.0857355{col 74}{space 3} .2957098
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .1739317{col 33}{space 2} .0524017{col 44}{space 1}    3.32{col 53}{space 3}0.001{col 61}{space 4} .0712263{col 74}{space 3} .2766371
{txt}{space 17}m1 {c |}{col 21}{res}{space 2} 2.612082{col 33}{space 2} .2365804{col 44}{space 1}   11.04{col 53}{space 3}0.000{col 61}{space 4} 2.148393{col 74}{space 3} 3.075771
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} 3.072445{col 33}{space 2} .2034624{col 44}{space 1}   15.10{col 53}{space 3}0.000{col 61}{space 4} 2.673667{col 74}{space 3} 3.471224
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}pais{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33}  .174077{col 44} .0456876{col 58}  .104073{col 70} .2911686
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 3.010506{col 44} .1047377{col 58} 2.812066{col 70} 3.222949
{txt}{hline 29}{c BT}{hline 48}

{p 0 9 2}Warning: Sampling weights were 
specified only at the first level 
in a multilevel model. If these weights are 
indicative of overall and not conditional 
inclusion probabilities, then 
{help mixed##sampling:results may be biased}.
{p_end}

{com}.                         outreg2 using a14.doc, dec(2) append ctitle(Direct)
{txt}{stata `"shellout using `"a14.doc"'"':a14.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a14.txt""':seeout}

{com}.         margins, dydx(direct) at(elviolence=(0(.25)1)) 
{res}
{txt}{col 1}Average marginal effects{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:31,280}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.direct}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 10:elviolence} = {res:{ralign 3:0}}
{lalign 7:2._at: }{space 0}{lalign 10:elviolence} = {res:{ralign 3:.25}}
{lalign 7:3._at: }{space 0}{lalign 10:elviolence} = {res:{ralign 3:.5}}
{lalign 7:4._at: }{space 0}{lalign 10:elviolence} = {res:{ralign 3:.75}}
{lalign 7:5._at: }{space 0}{lalign 10:elviolence} = {res:{ralign 3:1}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.direct    {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.direct     {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.3113669{col 26}{space 2} .1600079{col 37}{space 1}   -1.95{col 46}{space 3}0.052{col 54}{space 4}-.6249767{col 67}{space 3} .0022429
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.2499731{col 26}{space 2} .1055545{col 37}{space 1}   -2.37{col 46}{space 3}0.018{col 54}{space 4}-.4568562{col 67}{space 3}  -.04309
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1885792{col 26}{space 2} .0820259{col 37}{space 1}   -2.30{col 46}{space 3}0.022{col 54}{space 4}-.3493471{col 67}{space 3}-.0278114
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.1271854{col 26}{space 2} .1111846{col 37}{space 1}   -1.14{col 46}{space 3}0.253{col 54}{space 4}-.3451033{col 67}{space 3} .0907324
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.0657916{col 26}{space 2} .1674606{col 37}{space 1}   -0.39{col 46}{space 3}0.694{col 54}{space 4}-.3940084{col 67}{space 3} .2624252
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, xtitle(Electoral Violence) ///
>                 ytitle(Effects on Electoral Trust) ///
>                 title(Direct Exposure to Vote Buying) yline(0)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:elviolence}{p_end}
{res}{txt}
{com}.         graph save Graph "elviolence_direct.gph", replace
{res}{txt}file {bf:elviolence_direct.gph} saved

{com}. graph combine elviolence_direct.gph elviolence_indirect.gph
{res}{txt}
{com}.         graph save Graph "a14.gph", replace
{res}{txt}file {bf:a14.gph} saved

{com}.         
.         
. ***A15: moderation by voting irregularities
. mixed trustel i.indirect##c.irregularities woman quintall agecohort edr rural m1 if wave==2014 [pweight = weight1500] || pais: 
{res}
{txt}Obtaining starting values by EM ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-57692.678}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-57692.678}  (backed up)
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 53}Number of obs{col 69} = {res} 31,058
{txt}Group variable: {res}pais{col 53}{txt}Number of groups{col 69} = {res}     21
{txt}{col 53}Obs per group:
{col 66}min = {res}  1,176
{txt}{col 66}avg = {res}1,479.0
{txt}{col 66}max = {res}  2,860
{col 53}{txt}Wald chi2({res}9{txt}){col 69} = {res} 549.89
{txt}Log pseudolikelihood = {res}-57692.678{col 53}{txt}Prob > chi2{col 69} = {res} 0.0000

{txt}{ralign 91:(Std. err. adjusted for {res:21} clusters in {res:pais})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}                  trustel{col 27}{c |} Coefficient{col 39}  std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}1.indirect {c |}{col 27}{res}{space 2}-.3000847{col 39}{space 2} .1380669{col 50}{space 1}   -2.17{col 59}{space 3}0.030{col 67}{space 4}-.5706909{col 80}{space 3}-.0294784
{txt}{space 11}irregularities {c |}{col 27}{res}{space 2}-1.402025{col 39}{space 2} .4592427{col 50}{space 1}   -3.05{col 59}{space 3}0.002{col 67}{space 4}-2.302124{col 80}{space 3}-.5019253
{txt}{space 25} {c |}
indirect#c.irregularities {c |}
{space 23}1  {c |}{col 27}{res}{space 2} .2400317{col 39}{space 2} .3016311{col 50}{space 1}    0.80{col 59}{space 3}0.426{col 67}{space 4}-.3511543{col 80}{space 3} .8312177
{txt}{space 25} {c |}
{space 20}woman {c |}{col 27}{res}{space 2}-.0625823{col 39}{space 2} .0179201{col 50}{space 1}   -3.49{col 59}{space 3}0.000{col 67}{space 4}-.0977051{col 80}{space 3}-.0274596
{txt}{space 17}quintall {c |}{col 27}{res}{space 2}-.0144721{col 39}{space 2} .0498827{col 50}{space 1}   -0.29{col 59}{space 3}0.772{col 67}{space 4}-.1122404{col 80}{space 3} .0832962
{txt}{space 16}agecohort {c |}{col 27}{res}{space 2} .2734153{col 39}{space 2} .0939395{col 50}{space 1}    2.91{col 59}{space 3}0.004{col 67}{space 4} .0892973{col 80}{space 3} .4575333
{txt}{space 22}edr {c |}{col 27}{res}{space 2} .1175088{col 39}{space 2} .0993379{col 50}{space 1}    1.18{col 59}{space 3}0.237{col 67}{space 4}-.0771899{col 80}{space 3} .3122075
{txt}{space 20}rural {c |}{col 27}{res}{space 2}  .175217{col 39}{space 2} .0535582{col 50}{space 1}    3.27{col 59}{space 3}0.001{col 67}{space 4} .0702448{col 80}{space 3} .2801891
{txt}{space 23}m1 {c |}{col 27}{res}{space 2} 2.617724{col 39}{space 2}  .235639{col 50}{space 1}   11.11{col 59}{space 3}0.000{col 67}{space 4}  2.15588{col 80}{space 3} 3.079568
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} 2.964899{col 39}{space 2} .3180705{col 50}{space 1}    9.32{col 59}{space 3}0.000{col 67}{space 4} 2.341492{col 80}{space 3} 3.588306
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}pais{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33} .2823907{col 44} .0737949{col 58} .1692048{col 70} .4712898
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 3.013374{col 44} .1056517{col 58} 2.813256{col 70} 3.227729
{txt}{hline 29}{c BT}{hline 48}

{p 0 9 2}Warning: Sampling weights were 
specified only at the first level 
in a multilevel model. If these weights are 
indicative of overall and not conditional 
inclusion probabilities, then 
{help mixed##sampling:results may be biased}.
{p_end}

{com}.                         outreg2 using a15.doc, dec(2) replace ctitle(Indirect)
{txt}{stata `"shellout using `"a15.doc"'"':a15.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a15.txt""':seeout}

{com}.         margins, dydx(indirect) at(irregularities=(0(.25)1))
{res}
{txt}{col 1}Average marginal effects{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:31,058}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.indirect}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 14:irregularities} = {res:{ralign 3:0}}
{lalign 7:2._at: }{space 0}{lalign 14:irregularities} = {res:{ralign 3:.25}}
{lalign 7:3._at: }{space 0}{lalign 14:irregularities} = {res:{ralign 3:.5}}
{lalign 7:4._at: }{space 0}{lalign 14:irregularities} = {res:{ralign 3:.75}}
{lalign 7:5._at: }{space 0}{lalign 14:irregularities} = {res:{ralign 3:1}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.indirect  {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.indirect   {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.3000847{col 26}{space 2} .1380669{col 37}{space 1}   -2.17{col 46}{space 3}0.030{col 54}{space 4}-.5706909{col 67}{space 3}-.0294784
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.2400767{col 26}{space 2} .0741632{col 37}{space 1}   -3.24{col 46}{space 3}0.001{col 54}{space 4} -.385434{col 67}{space 3}-.0947195
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1800688{col 26}{space 2} .0575374{col 37}{space 1}   -3.13{col 46}{space 3}0.002{col 54}{space 4}-.2928401{col 67}{space 3}-.0672975
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.1200609{col 26}{space 2} .1117747{col 37}{space 1}   -1.07{col 46}{space 3}0.283{col 54}{space 4}-.3391353{col 67}{space 3} .0990136
{txt}{space 10}5  {c |}{col 14}{res}{space 2} -.060053{col 26}{space 2} .1817946{col 37}{space 1}   -0.33{col 46}{space 3}0.741{col 54}{space 4}-.4163639{col 67}{space 3}  .296258
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, xtitle(Voting Irregularities) ///
>                 ytitle(Effects on Electoral Trust) ///
>                 title(Indirect Exposure to Vote Buying) yline(0)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:irregularities}{p_end}
{res}{txt}
{com}.         graph save Graph "irregularities_indirect.gph", replace                         
{res}{txt}file {bf:irregularities_indirect.gph} saved

{com}. mixed trustel i.direct##c.irregularities woman quintall agecohort edr rural m1 if wave==2014 [pweight = weight1500] || pais: 
{res}
{txt}Obtaining starting values by EM ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-58104.232}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-58104.232}  (backed up)
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 53}Number of obs{col 69} = {res} 31,280
{txt}Group variable: {res}pais{col 53}{txt}Number of groups{col 69} = {res}     21
{txt}{col 53}Obs per group:
{col 66}min = {res}  1,285
{txt}{col 66}avg = {res}1,489.5
{txt}{col 66}max = {res}  2,862
{col 53}{txt}Wald chi2({res}9{txt}){col 69} = {res} 539.90
{txt}Log pseudolikelihood = {res}-58104.232{col 53}{txt}Prob > chi2{col 69} = {res} 0.0000

{txt}{ralign 89:(Std. err. adjusted for {res:21} clusters in {res:pais})}
{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}                trustel{col 25}{c |} Coefficient{col 37}  std. err.{col 49}      z{col 57}   P>|z|{col 65}     [95% con{col 78}f. interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}1.direct {c |}{col 25}{res}{space 2}-.1995763{col 37}{space 2} .1467757{col 48}{space 1}   -1.36{col 57}{space 3}0.174{col 65}{space 4}-.4872513{col 78}{space 3} .0880988
{txt}{space 9}irregularities {c |}{col 25}{res}{space 2}-1.385591{col 37}{space 2}  .453581{col 48}{space 1}   -3.05{col 57}{space 3}0.002{col 65}{space 4}-2.274593{col 78}{space 3}-.4965881
{txt}{space 23} {c |}
direct#c.irregularities {c |}
{space 21}1  {c |}{col 25}{res}{space 2} .0824962{col 37}{space 2} .3130701{col 48}{space 1}    0.26{col 57}{space 3}0.792{col 65}{space 4}-.5311099{col 78}{space 3} .6961022
{txt}{space 23} {c |}
{space 18}woman {c |}{col 25}{res}{space 2}-.0613367{col 37}{space 2} .0183059{col 48}{space 1}   -3.35{col 57}{space 3}0.001{col 65}{space 4}-.0972156{col 78}{space 3}-.0254577
{txt}{space 15}quintall {c |}{col 25}{res}{space 2}-.0075003{col 37}{space 2} .0496835{col 48}{space 1}   -0.15{col 57}{space 3}0.880{col 65}{space 4}-.1048783{col 78}{space 3} .0898776
{txt}{space 14}agecohort {c |}{col 25}{res}{space 2}   .27518{col 37}{space 2}  .093227{col 48}{space 1}    2.95{col 57}{space 3}0.003{col 65}{space 4} .0924584{col 78}{space 3} .4579017
{txt}{space 20}edr {c |}{col 25}{res}{space 2} .1053146{col 37}{space 2} .0974562{col 48}{space 1}    1.08{col 57}{space 3}0.280{col 65}{space 4} -.085696{col 78}{space 3} .2963253
{txt}{space 18}rural {c |}{col 25}{res}{space 2} .1742011{col 37}{space 2} .0525931{col 48}{space 1}    3.31{col 57}{space 3}0.001{col 65}{space 4} .0711206{col 78}{space 3} .2772815
{txt}{space 21}m1 {c |}{col 25}{res}{space 2} 2.612634{col 37}{space 2} .2368012{col 48}{space 1}   11.03{col 57}{space 3}0.000{col 65}{space 4} 2.148513{col 78}{space 3} 3.076756
{txt}{space 18}_cons {c |}{col 25}{res}{space 2} 2.952302{col 37}{space 2} .3161922{col 48}{space 1}    9.34{col 57}{space 3}0.000{col 65}{space 4} 2.332576{col 78}{space 3} 3.572027
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}pais{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33} .2865497{col 44} .0750153{col 58} .1715401{col 70} .4786681
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 3.010799{col 44} .1046457{col 58} 2.812528{col 70} 3.223048
{txt}{hline 29}{c BT}{hline 48}

{p 0 9 2}Warning: Sampling weights were 
specified only at the first level 
in a multilevel model. If these weights are 
indicative of overall and not conditional 
inclusion probabilities, then 
{help mixed##sampling:results may be biased}.
{p_end}

{com}.                         outreg2 using a15.doc, dec(2) append ctitle(Direct)
{txt}{stata `"shellout using `"a15.doc"'"':a15.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a15.txt""':seeout}

{com}.         margins, dydx(direct) at(irregularities=(0(.25)1)) 
{res}
{txt}{col 1}Average marginal effects{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:31,280}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.direct}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 14:irregularities} = {res:{ralign 3:0}}
{lalign 7:2._at: }{space 0}{lalign 14:irregularities} = {res:{ralign 3:.25}}
{lalign 7:3._at: }{space 0}{lalign 14:irregularities} = {res:{ralign 3:.5}}
{lalign 7:4._at: }{space 0}{lalign 14:irregularities} = {res:{ralign 3:.75}}
{lalign 7:5._at: }{space 0}{lalign 14:irregularities} = {res:{ralign 3:1}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.direct    {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.direct     {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.1995763{col 26}{space 2} .1467757{col 37}{space 1}   -1.36{col 46}{space 3}0.174{col 54}{space 4}-.4872513{col 67}{space 3} .0880988
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.1789522{col 26}{space 2} .0858355{col 37}{space 1}   -2.08{col 46}{space 3}0.037{col 54}{space 4}-.3471868{col 67}{space 3}-.0107177
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1583282{col 26}{space 2} .0737834{col 37}{space 1}   -2.15{col 46}{space 3}0.032{col 54}{space 4}-.3029409{col 67}{space 3}-.0137155
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.1377041{col 26}{space 2}  .125586{col 37}{space 1}   -1.10{col 46}{space 3}0.273{col 54}{space 4}-.3838481{col 67}{space 3} .1084398
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.1170801{col 26}{space 2} .1958349{col 37}{space 1}   -0.60{col 46}{space 3}0.550{col 54}{space 4}-.5009094{col 67}{space 3} .2667492
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot,  xtitle(Voting Irregularities) ///
>                 ytitle(Effects on Electoral Trust) ///
>                 title(Direct Exposure to Vote Buying) yline(0)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:irregularities}{p_end}
{res}{txt}
{com}.         graph save Graph "irregularities_direct.gph", replace
{res}{txt}file {bf:irregularities_direct.gph} saved

{com}. graph combine irregularities_direct.gph irregularities_indirect.gph
{res}{txt}
{com}.         graph save Graph "a15.gph", replace
{res}{txt}file {bf:a15.gph} saved

{com}. 
.         
. ***A16: moderation by government intimidation
. mixed trustel i.indirect##c.govtintimidation woman quintall agecohort edr rural m1 if wave==2014 [pweight = weight1500] || pais: 
{res}
{txt}Obtaining starting values by EM ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-57692.371}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-57692.371}  (backed up)
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 53}Number of obs{col 69} = {res} 31,058
{txt}Group variable: {res}pais{col 53}{txt}Number of groups{col 69} = {res}     21
{txt}{col 53}Obs per group:
{col 66}min = {res}  1,176
{txt}{col 66}avg = {res}1,479.0
{txt}{col 66}max = {res}  2,860
{col 53}{txt}Wald chi2({res}9{txt}){col 69} = {res} 744.20
{txt}Log pseudolikelihood = {res}-57692.371{col 53}{txt}Prob > chi2{col 69} = {res} 0.0000

{txt}{ralign 93:(Std. err. adjusted for {res:21} clusters in {res:pais})}
{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                    trustel{col 29}{c |} Coefficient{col 41}  std. err.{col 53}      z{col 61}   P>|z|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}1.indirect {c |}{col 29}{res}{space 2}-.3205784{col 41}{space 2} .1551413{col 52}{space 1}   -2.07{col 61}{space 3}0.039{col 69}{space 4}-.6246497{col 82}{space 3}-.0165071
{txt}{space 11}govtintimidation {c |}{col 29}{res}{space 2}-1.471103{col 41}{space 2} .6084795{col 52}{space 1}   -2.42{col 61}{space 3}0.016{col 69}{space 4}-2.663701{col 82}{space 3}-.2785053
{txt}{space 27} {c |}
indirect#c.govtintimidation {c |}
{space 25}1  {c |}{col 29}{res}{space 2} .3401555{col 41}{space 2} .3684408{col 52}{space 1}    0.92{col 61}{space 3}0.356{col 69}{space 4}-.3819752{col 82}{space 3} 1.062286
{txt}{space 27} {c |}
{space 22}woman {c |}{col 29}{res}{space 2}-.0627833{col 41}{space 2} .0179997{col 52}{space 1}   -3.49{col 61}{space 3}0.000{col 69}{space 4}-.0980621{col 82}{space 3}-.0275045
{txt}{space 19}quintall {c |}{col 29}{res}{space 2}-.0150193{col 41}{space 2} .0496651{col 52}{space 1}   -0.30{col 61}{space 3}0.762{col 69}{space 4}-.1123612{col 82}{space 3} .0823226
{txt}{space 18}agecohort {c |}{col 29}{res}{space 2} .2731697{col 41}{space 2} .0938519{col 52}{space 1}    2.91{col 61}{space 3}0.004{col 69}{space 4} .0892234{col 82}{space 3}  .457116
{txt}{space 24}edr {c |}{col 29}{res}{space 2} .1194372{col 41}{space 2}  .099063{col 52}{space 1}    1.21{col 61}{space 3}0.228{col 69}{space 4}-.0747226{col 82}{space 3} .3135971
{txt}{space 22}rural {c |}{col 29}{res}{space 2} .1755536{col 41}{space 2} .0534826{col 52}{space 1}    3.28{col 61}{space 3}0.001{col 69}{space 4} .0707296{col 82}{space 3} .2803775
{txt}{space 25}m1 {c |}{col 29}{res}{space 2}  2.61853{col 41}{space 2} .2350008{col 52}{space 1}   11.14{col 61}{space 3}0.000{col 69}{space 4} 2.157937{col 82}{space 3} 3.079123
{txt}{space 22}_cons {c |}{col 29}{res}{space 2}  2.87907{col 41}{space 2} .3287558{col 52}{space 1}    8.76{col 61}{space 3}0.000{col 69}{space 4} 2.234721{col 82}{space 3}  3.52342
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}pais{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33} .2976961{col 44} .0798586{col 58}  .175968{col 70} .5036313
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 3.013198{col 44} .1056881{col 58} 2.813013{col 70} 3.227629
{txt}{hline 29}{c BT}{hline 48}

{p 0 9 2}Warning: Sampling weights were 
specified only at the first level 
in a multilevel model. If these weights are 
indicative of overall and not conditional 
inclusion probabilities, then 
{help mixed##sampling:results may be biased}.
{p_end}

{com}.                         outreg2 using a16.doc, dec(2) replace ctitle(Indirect)
{txt}{stata `"shellout using `"a16.doc"'"':a16.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a16.txt""':seeout}

{com}.         margins, dydx(indirect) at(govtintimidation=(0(.25)1))
{res}
{txt}{col 1}Average marginal effects{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:31,058}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.indirect}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:govtintimidation} = {res:{ralign 3:0}}
{lalign 7:2._at: }{space 0}{lalign 16:govtintimidation} = {res:{ralign 3:.25}}
{lalign 7:3._at: }{space 0}{lalign 16:govtintimidation} = {res:{ralign 3:.5}}
{lalign 7:4._at: }{space 0}{lalign 16:govtintimidation} = {res:{ralign 3:.75}}
{lalign 7:5._at: }{space 0}{lalign 16:govtintimidation} = {res:{ralign 3:1}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.indirect  {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.indirect   {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.3205784{col 26}{space 2} .1551413{col 37}{space 1}   -2.07{col 46}{space 3}0.039{col 54}{space 4}-.6246497{col 67}{space 3}-.0165071
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.2355395{col 26}{space 2} .0814975{col 37}{space 1}   -2.89{col 46}{space 3}0.004{col 54}{space 4}-.3952718{col 67}{space 3}-.0758073
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1505007{col 26}{space 2}  .078635{col 37}{space 1}   -1.91{col 46}{space 3}0.056{col 54}{space 4}-.3046224{col 67}{space 3}  .003621
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0654618{col 26}{space 2} .1506441{col 37}{space 1}   -0.43{col 46}{space 3}0.664{col 54}{space 4}-.3607188{col 67}{space 3} .2297952
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0195771{col 26}{space 2} .2370072{col 37}{space 1}    0.08{col 46}{space 3}0.934{col 54}{space 4}-.4449485{col 67}{space 3} .4841026
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, xtitle(Government Intimidation) ///
>                 ytitle(Effects on Electoral Trust) ///
>                 title(Indirect Exposure to Vote Buying) yline(0)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:govtintimidation}{p_end}
{res}{txt}
{com}.         graph save Graph "govtintimidation_indirect.gph", replace                               
{res}{txt}file {bf:govtintimidation_indirect.gph} saved

{com}. mixed trustel i.direct##c.govtintimidation woman quintall agecohort edr rural m1 if wave==2014 [pweight = weight1500] || pais: 
{res}
{txt}Obtaining starting values by EM ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:  -58102.2}  
Iteration 1:{space 2}Log pseudolikelihood = {res:  -58102.2}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 53}Number of obs{col 69} = {res} 31,280
{txt}Group variable: {res}pais{col 53}{txt}Number of groups{col 69} = {res}     21
{txt}{col 53}Obs per group:
{col 66}min = {res}  1,285
{txt}{col 66}avg = {res}1,489.5
{txt}{col 66}max = {res}  2,862
{col 53}{txt}Wald chi2({res}9{txt}){col 69} = {res} 913.50
{txt}Log pseudolikelihood = {res}  -58102.2{col 53}{txt}Prob > chi2{col 69} = {res} 0.0000

{txt}{ralign 91:(Std. err. adjusted for {res:21} clusters in {res:pais})}
{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}                  trustel{col 27}{c |} Coefficient{col 39}  std. err.{col 51}      z{col 59}   P>|z|{col 67}     [95% con{col 80}f. interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}1.direct {c |}{col 27}{res}{space 2}-.3630017{col 39}{space 2} .1798845{col 50}{space 1}   -2.02{col 59}{space 3}0.044{col 67}{space 4} -.715569{col 80}{space 3}-.0104345
{txt}{space 9}govtintimidation {c |}{col 27}{res}{space 2}-1.461843{col 39}{space 2} .6039581{col 50}{space 1}   -2.42{col 59}{space 3}0.016{col 67}{space 4} -2.64558{col 80}{space 3}-.2781071
{txt}{space 25} {c |}
direct#c.govtintimidation {c |}
{space 23}1  {c |}{col 27}{res}{space 2} .4180375{col 39}{space 2} .3430826{col 50}{space 1}    1.22{col 59}{space 3}0.223{col 67}{space 4}-.2543921{col 80}{space 3} 1.090467
{txt}{space 25} {c |}
{space 20}woman {c |}{col 27}{res}{space 2} -.061579{col 39}{space 2} .0183779{col 50}{space 1}   -3.35{col 59}{space 3}0.001{col 67}{space 4}-.0975991{col 80}{space 3}-.0255589
{txt}{space 17}quintall {c |}{col 27}{res}{space 2}-.0090336{col 39}{space 2} .0492822{col 50}{space 1}   -0.18{col 59}{space 3}0.855{col 67}{space 4}-.1056248{col 80}{space 3} .0875577
{txt}{space 16}agecohort {c |}{col 27}{res}{space 2} .2753755{col 39}{space 2} .0930808{col 50}{space 1}    2.96{col 59}{space 3}0.003{col 67}{space 4} .0929405{col 80}{space 3} .4578104
{txt}{space 22}edr {c |}{col 27}{res}{space 2}  .108076{col 39}{space 2} .0968847{col 50}{space 1}    1.12{col 59}{space 3}0.265{col 67}{space 4}-.0818145{col 80}{space 3} .2979665
{txt}{space 20}rural {c |}{col 27}{res}{space 2} .1749781{col 39}{space 2} .0523724{col 50}{space 1}    3.34{col 59}{space 3}0.001{col 67}{space 4} .0723301{col 80}{space 3} .2776262
{txt}{space 23}m1 {c |}{col 27}{res}{space 2} 2.612837{col 39}{space 2} .2368767{col 50}{space 1}   11.03{col 59}{space 3}0.000{col 67}{space 4} 2.148567{col 80}{space 3} 3.077107
{txt}{space 20}_cons {c |}{col 27}{res}{space 2} 2.870935{col 39}{space 2} .3280944{col 50}{space 1}    8.75{col 59}{space 3}0.000{col 67}{space 4} 2.227882{col 80}{space 3} 3.513988
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}pais{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33} .3018225{col 44} .0807103{col 58} .1787031{col 70} .5097664
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 3.010273{col 44} .1048565{col 58} 2.811617{col 70} 3.222966
{txt}{hline 29}{c BT}{hline 48}

{p 0 9 2}Warning: Sampling weights were 
specified only at the first level 
in a multilevel model. If these weights are 
indicative of overall and not conditional 
inclusion probabilities, then 
{help mixed##sampling:results may be biased}.
{p_end}

{com}.                         outreg2 using a16.doc, dec(2) append ctitle(Direct)
{txt}{stata `"shellout using `"a16.doc"'"':a16.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a16.txt""':seeout}

{com}.         margins, dydx(direct) at(govtintimidation=(0(.25)1)) 
{res}
{txt}{col 1}Average marginal effects{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:31,280}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.direct}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:govtintimidation} = {res:{ralign 3:0}}
{lalign 7:2._at: }{space 0}{lalign 16:govtintimidation} = {res:{ralign 3:.25}}
{lalign 7:3._at: }{space 0}{lalign 16:govtintimidation} = {res:{ralign 3:.5}}
{lalign 7:4._at: }{space 0}{lalign 16:govtintimidation} = {res:{ralign 3:.75}}
{lalign 7:5._at: }{space 0}{lalign 16:govtintimidation} = {res:{ralign 3:1}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.direct    {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.direct     {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.3630017{col 26}{space 2} .1798845{col 37}{space 1}   -2.02{col 46}{space 3}0.044{col 54}{space 4} -.715569{col 67}{space 3}-.0104345
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.2584924{col 26}{space 2} .1152035{col 37}{space 1}   -2.24{col 46}{space 3}0.025{col 54}{space 4}-.4842872{col 67}{space 3}-.0326976
{txt}{space 10}3  {c |}{col 14}{res}{space 2} -.153983{col 26}{space 2} .0943317{col 37}{space 1}   -1.63{col 46}{space 3}0.103{col 54}{space 4}-.3388698{col 67}{space 3} .0309038
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0494736{col 26}{space 2} .1387022{col 37}{space 1}   -0.36{col 46}{space 3}0.721{col 54}{space 4} -.321325{col 67}{space 3} .2223777
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0550358{col 26}{space 2} .2104551{col 37}{space 1}    0.26{col 46}{space 3}0.794{col 54}{space 4}-.3574487{col 67}{space 3} .4675202
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, xtitle(Government Intimidation) ///
>                 ytitle(Effects on Electoral Trust) ///
>                 title(Direct Exposure to Vote Buying) yline(0)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:govtintimidation}{p_end}
{res}{txt}
{com}.         graph save Graph "govtintimidation_direct.gph", replace
{res}{txt}file {bf:govtintimidation_direct.gph} saved

{com}. graph combine govtintimidation_direct.gph govtintimidation_indirect.gph
{res}{txt}
{com}.         graph save Graph "a16.gph", replace
{res}{txt}file {bf:a16.gph} saved

{com}. 
.         
. ***A17: moderation by particularistic goods
. mixed trustel i.indirect##c.particular woman quintall agecohort edr rural m1 if wave==2014 [pweight = weight1500] || pais: 
{res}
{txt}Obtaining starting values by EM ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-57689.183}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-57689.183}  (backed up)
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 53}Number of obs{col 69} = {res} 31,058
{txt}Group variable: {res}pais{col 53}{txt}Number of groups{col 69} = {res}     21
{txt}{col 53}Obs per group:
{col 66}min = {res}  1,176
{txt}{col 66}avg = {res}1,479.0
{txt}{col 66}max = {res}  2,860
{col 53}{txt}Wald chi2({res}9{txt}){col 69} = {res} 552.36
{txt}Log pseudolikelihood = {res}-57689.183{col 53}{txt}Prob > chi2{col 69} = {res} 0.0000

{txt}{ralign 87:(Std. err. adjusted for {res:21} clusters in {res:pais})}
{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}              trustel{col 23}{c |} Coefficient{col 35}  std. err.{col 47}      z{col 55}   P>|z|{col 63}     [95% con{col 76}f. interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}1.indirect {c |}{col 23}{res}{space 2}-.4239527{col 35}{space 2}   .12889{col 46}{space 1}   -3.29{col 55}{space 3}0.001{col 63}{space 4}-.6765726{col 76}{space 3}-.1713329
{txt}{space 11}particular {c |}{col 23}{res}{space 2} -1.52498{col 35}{space 2} .6058943{col 46}{space 1}   -2.52{col 55}{space 3}0.012{col 63}{space 4}-2.712511{col 76}{space 3}-.3374486
{txt}{space 21} {c |}
indirect#c.particular {c |}
{space 19}1  {c |}{col 23}{res}{space 2} .4759884{col 35}{space 2} .2855228{col 46}{space 1}    1.67{col 55}{space 3}0.095{col 63}{space 4}-.0836259{col 76}{space 3} 1.035603
{txt}{space 21} {c |}
{space 16}woman {c |}{col 23}{res}{space 2}-.0630038{col 35}{space 2} .0180709{col 46}{space 1}   -3.49{col 55}{space 3}0.000{col 63}{space 4} -.098422{col 76}{space 3}-.0275855
{txt}{space 13}quintall {c |}{col 23}{res}{space 2}-.0152207{col 35}{space 2} .0498887{col 46}{space 1}   -0.31{col 55}{space 3}0.760{col 63}{space 4}-.1130008{col 76}{space 3} .0825595
{txt}{space 12}agecohort {c |}{col 23}{res}{space 2} .2718482{col 35}{space 2} .0937348{col 46}{space 1}    2.90{col 55}{space 3}0.004{col 63}{space 4} .0881315{col 76}{space 3} .4555649
{txt}{space 18}edr {c |}{col 23}{res}{space 2} .1205118{col 35}{space 2} .0995085{col 46}{space 1}    1.21{col 55}{space 3}0.226{col 63}{space 4}-.0745213{col 76}{space 3} .3155448
{txt}{space 16}rural {c |}{col 23}{res}{space 2} .1750692{col 35}{space 2} .0538524{col 46}{space 1}    3.25{col 55}{space 3}0.001{col 63}{space 4} .0695204{col 76}{space 3} .2806179
{txt}{space 19}m1 {c |}{col 23}{res}{space 2} 2.617327{col 35}{space 2} .2357145{col 46}{space 1}   11.10{col 55}{space 3}0.000{col 63}{space 4} 2.155335{col 76}{space 3} 3.079319
{txt}{space 16}_cons {c |}{col 23}{res}{space 2}  2.99618{col 35}{space 2} .3680036{col 46}{space 1}    8.14{col 55}{space 3}0.000{col 63}{space 4} 2.274906{col 76}{space 3} 3.717454
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}pais{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33} .3026517{col 44} .0794571{col 58} .1809138{col 70} .5063078
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 3.012505{col 44} .1055242{col 58} 2.812621{col 70} 3.226594
{txt}{hline 29}{c BT}{hline 48}

{p 0 9 2}Warning: Sampling weights were 
specified only at the first level 
in a multilevel model. If these weights are 
indicative of overall and not conditional 
inclusion probabilities, then 
{help mixed##sampling:results may be biased}.
{p_end}

{com}.         outreg2 using a17.doc, dec(2) replace ctitle(Indirect)
{txt}{stata `"shellout using `"a17.doc"'"':a17.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a17.txt""':seeout}

{com}.         margins, dydx(indirect) at(particular=(0(.25)1))
{res}
{txt}{col 1}Average marginal effects{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:31,058}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.indirect}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 10:particular} = {res:{ralign 3:0}}
{lalign 7:2._at: }{space 0}{lalign 10:particular} = {res:{ralign 3:.25}}
{lalign 7:3._at: }{space 0}{lalign 10:particular} = {res:{ralign 3:.5}}
{lalign 7:4._at: }{space 0}{lalign 10:particular} = {res:{ralign 3:.75}}
{lalign 7:5._at: }{space 0}{lalign 10:particular} = {res:{ralign 3:1}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.indirect  {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.indirect   {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.4239527{col 26}{space 2}   .12889{col 37}{space 1}   -3.29{col 46}{space 3}0.001{col 54}{space 4}-.6765726{col 67}{space 3}-.1713329
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.3049556{col 26}{space 2} .0724067{col 37}{space 1}   -4.21{col 46}{space 3}0.000{col 54}{space 4}-.4468702{col 67}{space 3} -.163041
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1859585{col 26}{space 2} .0637435{col 37}{space 1}   -2.92{col 46}{space 3}0.004{col 54}{space 4}-.3108934{col 67}{space 3}-.0610236
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0669614{col 26}{space 2} .1143422{col 37}{space 1}   -0.59{col 46}{space 3}0.558{col 54}{space 4} -.291068{col 67}{space 3} .1571452
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0520357{col 26}{space 2} .1796537{col 37}{space 1}    0.29{col 46}{space 3}0.772{col 54}{space 4} -.300079{col 67}{space 3} .4041505
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) ///
>                 xtitle(Prevalence of Particularistic Goods) ///
>                 ytitle(Effects on Trust in Elections) ///
>                 title(Indirect Exposure to Vote Buying)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:particular}{p_end}
{res}{txt}
{com}.         graph save Graph "fig1particular_indirect.gph", replace                         
{res}{txt}file {bf:fig1particular_indirect.gph} saved

{com}. mixed trustel i.direct##c.particular woman quintall agecohort edr rural m1 if wave==2014 [pweight = weight1500] || pais: 
{res}
{txt}Obtaining starting values by EM ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-58097.556}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-58097.556}  (backed up)
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 53}Number of obs{col 69} = {res} 31,280
{txt}Group variable: {res}pais{col 53}{txt}Number of groups{col 69} = {res}     21
{txt}{col 53}Obs per group:
{col 66}min = {res}  1,285
{txt}{col 66}avg = {res}1,489.5
{txt}{col 66}max = {res}  2,862
{col 53}{txt}Wald chi2({res}9{txt}){col 69} = {res} 600.14
{txt}Log pseudolikelihood = {res}-58097.556{col 53}{txt}Prob > chi2{col 69} = {res} 0.0000

{txt}{ralign 85:(Std. err. adjusted for {res:21} clusters in {res:pais})}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}            trustel{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      z{col 53}   P>|z|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 11}1.direct {c |}{col 21}{res}{space 2}-.5400454{col 33}{space 2} .1767169{col 44}{space 1}   -3.06{col 53}{space 3}0.002{col 61}{space 4}-.8864041{col 74}{space 3}-.1936867
{txt}{space 9}particular {c |}{col 21}{res}{space 2}-1.512244{col 33}{space 2} .6012277{col 44}{space 1}   -2.52{col 53}{space 3}0.012{col 61}{space 4}-2.690628{col 74}{space 3}-.3338591
{txt}{space 19} {c |}
direct#c.particular {c |}
{space 17}1  {c |}{col 21}{res}{space 2} .6582396{col 33}{space 2} .3236242{col 44}{space 1}    2.03{col 53}{space 3}0.042{col 61}{space 4} .0239479{col 74}{space 3} 1.292531
{txt}{space 19} {c |}
{space 14}woman {c |}{col 21}{res}{space 2}-.0617574{col 33}{space 2} .0182657{col 44}{space 1}   -3.38{col 53}{space 3}0.001{col 61}{space 4}-.0975576{col 74}{space 3}-.0259572
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}-.0095157{col 33}{space 2}  .049964{col 44}{space 1}   -0.19{col 53}{space 3}0.849{col 61}{space 4}-.1074434{col 74}{space 3} .0884119
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2} .2738538{col 33}{space 2} .0929797{col 44}{space 1}    2.95{col 53}{space 3}0.003{col 61}{space 4} .0916168{col 74}{space 3} .4560907
{txt}{space 16}edr {c |}{col 21}{res}{space 2} .1088198{col 33}{space 2} .0968927{col 44}{space 1}    1.12{col 53}{space 3}0.261{col 61}{space 4}-.0810863{col 74}{space 3} .2987259
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .1745762{col 33}{space 2} .0527304{col 44}{space 1}    3.31{col 53}{space 3}0.001{col 61}{space 4} .0712265{col 74}{space 3} .2779259
{txt}{space 17}m1 {c |}{col 21}{res}{space 2} 2.612571{col 33}{space 2} .2370047{col 44}{space 1}   11.02{col 53}{space 3}0.000{col 61}{space 4}  2.14805{col 74}{space 3} 3.077092
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} 2.985593{col 33}{space 2} .3678963{col 44}{space 1}    8.12{col 53}{space 3}0.000{col 61}{space 4} 2.264529{col 74}{space 3} 3.706656
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}pais{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33} .3076783{col 44} .0805551{col 58} .1841784{col 70} .5139906
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 3.009283{col 44} .1044413{col 58} 2.811389{col 70} 3.221107
{txt}{hline 29}{c BT}{hline 48}

{p 0 9 2}Warning: Sampling weights were 
specified only at the first level 
in a multilevel model. If these weights are 
indicative of overall and not conditional 
inclusion probabilities, then 
{help mixed##sampling:results may be biased}.
{p_end}

{com}.         outreg2 using a17.doc, dec(2) append ctitle(Direct)
{txt}{stata `"shellout using `"a17.doc"'"':a17.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a17.txt""':seeout}

{com}.         margins, dydx(direct) at(particular=(0(.25)1)) 
{res}
{txt}{col 1}Average marginal effects{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:31,280}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.direct}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 10:particular} = {res:{ralign 3:0}}
{lalign 7:2._at: }{space 0}{lalign 10:particular} = {res:{ralign 3:.25}}
{lalign 7:3._at: }{space 0}{lalign 10:particular} = {res:{ralign 3:.5}}
{lalign 7:4._at: }{space 0}{lalign 10:particular} = {res:{ralign 3:.75}}
{lalign 7:5._at: }{space 0}{lalign 10:particular} = {res:{ralign 3:1}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.direct    {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.direct     {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.5400454{col 26}{space 2} .1767169{col 37}{space 1}   -3.06{col 46}{space 3}0.002{col 54}{space 4}-.8864041{col 67}{space 3}-.1936867
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.3754855{col 26}{space 2} .1116901{col 37}{space 1}   -3.36{col 46}{space 3}0.001{col 54}{space 4}-.5943941{col 67}{space 3}-.1565769
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.2109256{col 26}{space 2} .0825353{col 37}{space 1}   -2.56{col 46}{space 3}0.011{col 54}{space 4}-.3726919{col 67}{space 3}-.0491594
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0463657{col 26}{space 2} .1193359{col 37}{space 1}   -0.39{col 46}{space 3}0.698{col 54}{space 4}-.2802598{col 67}{space 3} .1875283
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .1181942{col 26}{space 2} .1864446{col 37}{space 1}    0.63{col 46}{space 3}0.526{col 54}{space 4}-.2472306{col 67}{space 3} .4836189
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) ///
>                 xtitle(Prevalence of Particularistic Goods) ///
>                 ytitle(Effects on Trust in Elections) ///
>                 title(Direct Exposure to Vote Buying)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:particular}{p_end}
{res}{txt}
{com}.         graph save Graph "fig1particular_direct.gph", replace
{res}{txt}file {bf:fig1particular_direct.gph} saved

{com}. graph combine fig1particular_direct.gph fig1particular_indirect.gph
{res}{txt}
{com}.         graph save Graph "a17.gph", replace
{res}{txt}file {bf:a17.gph} saved

{com}. 
.         
. ***A18: moderation by clientelist linkages
. mixed trustel i.indirect##c.clientelist woman quintall agecohort edr rural m1 if wave==2014 [pweight = weight1500] || pais: 
{res}
{txt}Obtaining starting values by EM ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-57692.508}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-57692.508}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 53}Number of obs{col 69} = {res} 31,058
{txt}Group variable: {res}pais{col 53}{txt}Number of groups{col 69} = {res}     21
{txt}{col 53}Obs per group:
{col 66}min = {res}  1,176
{txt}{col 66}avg = {res}1,479.0
{txt}{col 66}max = {res}  2,860
{col 53}{txt}Wald chi2({res}9{txt}){col 69} = {res} 421.85
{txt}Log pseudolikelihood = {res}-57692.508{col 53}{txt}Prob > chi2{col 69} = {res} 0.0000

{txt}{ralign 88:(Std. err. adjusted for {res:21} clusters in {res:pais})}
{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}               trustel{col 24}{c |} Coefficient{col 36}  std. err.{col 48}      z{col 56}   P>|z|{col 64}     [95% con{col 77}f. interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}1.indirect {c |}{col 24}{res}{space 2}-.3139211{col 36}{space 2}  .138978{col 47}{space 1}   -2.26{col 56}{space 3}0.024{col 64}{space 4} -.586313{col 77}{space 3}-.0415292
{txt}{space 11}clientelist {c |}{col 24}{res}{space 2}-1.305806{col 36}{space 2} .4038371{col 47}{space 1}   -3.23{col 56}{space 3}0.001{col 64}{space 4}-2.097312{col 77}{space 3}-.5143002
{txt}{space 22} {c |}
indirect#c.clientelist {c |}
{space 20}1  {c |}{col 24}{res}{space 2}  .227823{col 36}{space 2} .2340808{col 47}{space 1}    0.97{col 56}{space 3}0.330{col 64}{space 4}-.2309669{col 77}{space 3} .6866129
{txt}{space 22} {c |}
{space 17}woman {c |}{col 24}{res}{space 2}-.0625904{col 36}{space 2} .0180965{col 47}{space 1}   -3.46{col 56}{space 3}0.001{col 64}{space 4}-.0980588{col 77}{space 3} -.027122
{txt}{space 14}quintall {c |}{col 24}{res}{space 2}-.0149828{col 36}{space 2} .0499596{col 47}{space 1}   -0.30{col 56}{space 3}0.764{col 64}{space 4}-.1129019{col 77}{space 3} .0829363
{txt}{space 13}agecohort {c |}{col 24}{res}{space 2}  .273218{col 36}{space 2} .0937151{col 47}{space 1}    2.92{col 56}{space 3}0.004{col 64}{space 4} .0895398{col 77}{space 3} .4568961
{txt}{space 19}edr {c |}{col 24}{res}{space 2} .1202797{col 36}{space 2} .0998045{col 47}{space 1}    1.21{col 56}{space 3}0.228{col 64}{space 4}-.0753334{col 77}{space 3} .3158929
{txt}{space 17}rural {c |}{col 24}{res}{space 2}  .175345{col 36}{space 2} .0538579{col 47}{space 1}    3.26{col 56}{space 3}0.001{col 64}{space 4} .0697855{col 77}{space 3} .2809045
{txt}{space 20}m1 {c |}{col 24}{res}{space 2} 2.615734{col 36}{space 2} .2362846{col 47}{space 1}   11.07{col 56}{space 3}0.000{col 64}{space 4} 2.152625{col 77}{space 3} 3.078844
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} 3.058014{col 36}{space 2} .3381666{col 47}{space 1}    9.04{col 56}{space 3}0.000{col 64}{space 4}  2.39522{col 77}{space 3} 3.720809
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}pais{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33}  .281307{col 44} .0804217{col 58} .1606328{col 70} .4926371
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 3.013348{col 44} .1056964{col 58} 2.813147{col 70} 3.227796
{txt}{hline 29}{c BT}{hline 48}

{p 0 9 2}Warning: Sampling weights were 
specified only at the first level 
in a multilevel model. If these weights are 
indicative of overall and not conditional 
inclusion probabilities, then 
{help mixed##sampling:results may be biased}.
{p_end}

{com}.         outreg2 using a18.doc, dec(2) replace ctitle(Indirect)
{txt}{stata `"shellout using `"a18.doc"'"':a18.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a18.txt""':seeout}

{com}.         margins, dydx(indirect) at(clientelist=(0(.25)1))
{res}
{txt}{col 1}Average marginal effects{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:31,058}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.indirect}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 11:clientelist} = {res:{ralign 3:0}}
{lalign 7:2._at: }{space 0}{lalign 11:clientelist} = {res:{ralign 3:.25}}
{lalign 7:3._at: }{space 0}{lalign 11:clientelist} = {res:{ralign 3:.5}}
{lalign 7:4._at: }{space 0}{lalign 11:clientelist} = {res:{ralign 3:.75}}
{lalign 7:5._at: }{space 0}{lalign 11:clientelist} = {res:{ralign 3:1}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.indirect  {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.indirect   {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.3139211{col 26}{space 2}  .138978{col 37}{space 1}   -2.26{col 46}{space 3}0.024{col 54}{space 4} -.586313{col 67}{space 3}-.0415292
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.2569654{col 26}{space 2}  .091591{col 37}{space 1}   -2.81{col 46}{space 3}0.005{col 54}{space 4}-.4364803{col 67}{space 3}-.0774504
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.2000096{col 26}{space 2}  .065667{col 37}{space 1}   -3.05{col 46}{space 3}0.002{col 54}{space 4}-.3287145{col 67}{space 3}-.0713047
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.1430539{col 26}{space 2} .0841702{col 37}{space 1}   -1.70{col 46}{space 3}0.089{col 54}{space 4}-.3080245{col 67}{space 3} .0219168
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.0860981{col 26}{space 2}  .129253{col 37}{space 1}   -0.67{col 46}{space 3}0.505{col 54}{space 4}-.3394293{col 67}{space 3} .1672331
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) ///
>                 xtitle(Prevalence of Clientelistic Party Linkages) ///
>                 ytitle(Effects on Trust in Elections) ///
>                 title(Indirect Exposure to Vote Buying)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:clientelist}{p_end}
{res}{txt}
{com}.         graph save Graph "fig1clientelist_indirect.gph", replace                                
{res}{txt}file {bf:fig1clientelist_indirect.gph} saved

{com}. mixed trustel i.direct##c.clientelist woman quintall agecohort edr rural m1 if wave==2014 [pweight = weight1500] || pais: 
{res}
{txt}Obtaining starting values by EM ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-58103.352}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-58103.352}  
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 53}Number of obs{col 69} = {res} 31,280
{txt}Group variable: {res}pais{col 53}{txt}Number of groups{col 69} = {res}     21
{txt}{col 53}Obs per group:
{col 66}min = {res}  1,285
{txt}{col 66}avg = {res}1,489.5
{txt}{col 66}max = {res}  2,862
{col 53}{txt}Wald chi2({res}9{txt}){col 69} = {res} 509.90
{txt}Log pseudolikelihood = {res}-58103.352{col 53}{txt}Prob > chi2{col 69} = {res} 0.0000

{txt}{ralign 86:(Std. err. adjusted for {res:21} clusters in {res:pais})}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}             trustel{col 22}{c |} Coefficient{col 34}  std. err.{col 46}      z{col 54}   P>|z|{col 62}     [95% con{col 75}f. interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}1.direct {c |}{col 22}{res}{space 2}-.2962884{col 34}{space 2} .1297691{col 45}{space 1}   -2.28{col 54}{space 3}0.022{col 62}{space 4}-.5506311{col 75}{space 3}-.0419458
{txt}{space 9}clientelist {c |}{col 22}{res}{space 2}-1.294537{col 34}{space 2} .4016469{col 45}{space 1}   -3.22{col 54}{space 3}0.001{col 62}{space 4} -2.08175{col 75}{space 3}-.5073232
{txt}{space 20} {c |}
direct#c.clientelist {c |}
{space 18}1  {c |}{col 22}{res}{space 2} .2008236{col 34}{space 2} .2676105{col 45}{space 1}    0.75{col 54}{space 3}0.453{col 62}{space 4}-.3236833{col 75}{space 3} .7253305
{txt}{space 20} {c |}
{space 15}woman {c |}{col 22}{res}{space 2}-.0613576{col 34}{space 2} .0184099{col 45}{space 1}   -3.33{col 54}{space 3}0.001{col 62}{space 4}-.0974403{col 75}{space 3} -.025275
{txt}{space 12}quintall {c |}{col 22}{res}{space 2}  -.00831{col 34}{space 2} .0500734{col 45}{space 1}   -0.17{col 54}{space 3}0.868{col 62}{space 4} -.106452{col 75}{space 3}  .089832
{txt}{space 11}agecohort {c |}{col 22}{res}{space 2} .2753813{col 34}{space 2} .0930522{col 45}{space 1}    2.96{col 54}{space 3}0.003{col 62}{space 4} .0930024{col 75}{space 3} .4577602
{txt}{space 17}edr {c |}{col 22}{res}{space 2} .1076351{col 34}{space 2} .0976497{col 45}{space 1}    1.10{col 54}{space 3}0.270{col 62}{space 4}-.0837548{col 75}{space 3} .2990251
{txt}{space 15}rural {c |}{col 22}{res}{space 2} .1746126{col 34}{space 2} .0527311{col 45}{space 1}    3.31{col 54}{space 3}0.001{col 62}{space 4} .0712615{col 75}{space 3} .2779637
{txt}{space 18}m1 {c |}{col 22}{res}{space 2} 2.611231{col 34}{space 2} .2374249{col 45}{space 1}   11.00{col 54}{space 3}0.000{col 62}{space 4} 2.145887{col 75}{space 3} 3.076576
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} 3.046535{col 34}{space 2} .3391927{col 45}{space 1}    8.98{col 54}{space 3}0.000{col 62}{space 4}  2.38173{col 75}{space 3} 3.711341
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}pais{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33} .2864759{col 44}   .08205{col 58} .1634158{col 70} .5022063
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33}  3.01062{col 44} .1047164{col 58} 2.812219{col 70} 3.223018
{txt}{hline 29}{c BT}{hline 48}

{p 0 9 2}Warning: Sampling weights were 
specified only at the first level 
in a multilevel model. If these weights are 
indicative of overall and not conditional 
inclusion probabilities, then 
{help mixed##sampling:results may be biased}.
{p_end}

{com}.         outreg2 using a18.doc, dec(2) append ctitle(Direct)
{txt}{stata `"shellout using `"a18.doc"'"':a18.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a18.txt""':seeout}

{com}.         margins, dydx(direct) at(clientelist=(0(.25)1)) 
{res}
{txt}{col 1}Average marginal effects{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:31,280}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.direct}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 11:clientelist} = {res:{ralign 3:0}}
{lalign 7:2._at: }{space 0}{lalign 11:clientelist} = {res:{ralign 3:.25}}
{lalign 7:3._at: }{space 0}{lalign 11:clientelist} = {res:{ralign 3:.5}}
{lalign 7:4._at: }{space 0}{lalign 11:clientelist} = {res:{ralign 3:.75}}
{lalign 7:5._at: }{space 0}{lalign 11:clientelist} = {res:{ralign 3:1}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.direct    {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.direct     {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.2962884{col 26}{space 2} .1297691{col 37}{space 1}   -2.28{col 46}{space 3}0.022{col 54}{space 4}-.5506311{col 67}{space 3}-.0419458
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.2460825{col 26}{space 2} .0751542{col 37}{space 1}   -3.27{col 46}{space 3}0.001{col 54}{space 4}-.3933821{col 67}{space 3} -.098783
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1958766{col 26}{space 2}   .05838{col 37}{space 1}   -3.36{col 46}{space 3}0.001{col 54}{space 4}-.3102993{col 67}{space 3}-.0814539
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.1456707{col 26}{space 2} .1005993{col 37}{space 1}   -1.45{col 46}{space 3}0.148{col 54}{space 4}-.3428417{col 67}{space 3} .0515003
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.0954648{col 26}{space 2} .1605744{col 37}{space 1}   -0.59{col 46}{space 3}0.552{col 54}{space 4}-.4101848{col 67}{space 3} .2192552
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) ///
>                 xtitle(Prevalence of Clientelistic Party Linkages) ///
>                 ytitle(Effects on Trust in Elections) ///
>                 title(Direct Exposure to Vote Buying)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:clientelist}{p_end}
{res}{txt}
{com}.         graph save Graph "fig1clientelist_direct.gph", replace
{res}{txt}file {bf:fig1clientelist_direct.gph} saved

{com}. graph combine fig1clientelist_direct.gph fig1clientelist_indirect.gph
{res}{txt}
{com}.         graph save Graph "a18.gph", replace
{res}{txt}file {bf:a18.gph} saved

{com}. 
. 
. ***A19: moderation by vote buying prevalence
. mixed trustel i.indirect##c.vbuy woman quintall agecohort edr rural m1 if wave==2014 [pweight = weight1500] || pais: 
{res}
{txt}Obtaining starting values by EM ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-57690.658}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-57690.658}  (backed up)
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 53}Number of obs{col 69} = {res} 31,058
{txt}Group variable: {res}pais{col 53}{txt}Number of groups{col 69} = {res}     21
{txt}{col 53}Obs per group:
{col 66}min = {res}  1,176
{txt}{col 66}avg = {res}1,479.0
{txt}{col 66}max = {res}  2,860
{col 53}{txt}Wald chi2({res}9{txt}){col 69} = {res} 734.47
{txt}Log pseudolikelihood = {res}-57690.658{col 53}{txt}Prob > chi2{col 69} = {res} 0.0000

{txt}{ralign 81:(Std. err. adjusted for {res:21} clusters in {res:pais})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}        trustel{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}1.indirect {c |}{col 17}{res}{space 2}-.2331948{col 29}{space 2} .1946788{col 40}{space 1}   -1.20{col 49}{space 3}0.231{col 57}{space 4}-.6147584{col 70}{space 3} .1483687
{txt}{space 11}vbuy {c |}{col 17}{res}{space 2}-2.151897{col 29}{space 2} .3548672{col 40}{space 1}   -6.06{col 49}{space 3}0.000{col 57}{space 4}-2.847424{col 70}{space 3} -1.45637
{txt}{space 15} {c |}
indirect#c.vbuy {c |}
{space 13}1  {c |}{col 17}{res}{space 2}  .122356{col 29}{space 2} .3632225{col 40}{space 1}    0.34{col 49}{space 3}0.736{col 57}{space 4} -.589547{col 70}{space 3}  .834259
{txt}{space 15} {c |}
{space 10}woman {c |}{col 17}{res}{space 2}-.0628808{col 29}{space 2} .0180366{col 40}{space 1}   -3.49{col 49}{space 3}0.000{col 57}{space 4}-.0982319{col 70}{space 3}-.0275297
{txt}{space 7}quintall {c |}{col 17}{res}{space 2}-.0143656{col 29}{space 2} .0499763{col 40}{space 1}   -0.29{col 49}{space 3}0.774{col 57}{space 4}-.1123174{col 70}{space 3} .0835863
{txt}{space 6}agecohort {c |}{col 17}{res}{space 2} .2730642{col 29}{space 2} .0938311{col 40}{space 1}    2.91{col 49}{space 3}0.004{col 57}{space 4} .0891585{col 70}{space 3} .4569699
{txt}{space 12}edr {c |}{col 17}{res}{space 2} .1175205{col 29}{space 2} .1002198{col 40}{space 1}    1.17{col 49}{space 3}0.241{col 57}{space 4}-.0789067{col 70}{space 3} .3139477
{txt}{space 10}rural {c |}{col 17}{res}{space 2} .1742909{col 29}{space 2} .0539118{col 40}{space 1}    3.23{col 49}{space 3}0.001{col 57}{space 4} .0686256{col 70}{space 3} .2799561
{txt}{space 13}m1 {c |}{col 17}{res}{space 2} 2.616051{col 29}{space 2} .2359272{col 40}{space 1}   11.09{col 49}{space 3}0.000{col 57}{space 4} 2.153642{col 70}{space 3}  3.07846
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 3.441939{col 29}{space 2} .2395307{col 40}{space 1}   14.37{col 49}{space 3}0.000{col 57}{space 4} 2.972467{col 70}{space 3}  3.91141
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}pais{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33} .1953209{col 44} .0543583{col 58} .1132028{col 70}  .337008
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 3.013749{col 44} .1055336{col 58} 2.813845{col 70} 3.227854
{txt}{hline 29}{c BT}{hline 48}

{p 0 9 2}Warning: Sampling weights were 
specified only at the first level 
in a multilevel model. If these weights are 
indicative of overall and not conditional 
inclusion probabilities, then 
{help mixed##sampling:results may be biased}.
{p_end}

{com}.         outreg2 using a19.doc, dec(2) replace ctitle(Indirect)
{txt}{stata `"shellout using `"a19.doc"'"':a19.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a19.txt""':seeout}

{com}.         margins, dydx(indirect) at(vbuy=(0(.25)1))
{res}
{txt}{col 1}Average marginal effects{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:31,058}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.indirect}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 4:vbuy} = {res:{ralign 3:0}}
{lalign 7:2._at: }{space 0}{lalign 4:vbuy} = {res:{ralign 3:.25}}
{lalign 7:3._at: }{space 0}{lalign 4:vbuy} = {res:{ralign 3:.5}}
{lalign 7:4._at: }{space 0}{lalign 4:vbuy} = {res:{ralign 3:.75}}
{lalign 7:5._at: }{space 0}{lalign 4:vbuy} = {res:{ralign 3:1}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.indirect  {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.indirect   {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.2331948{col 26}{space 2} .1946788{col 37}{space 1}   -1.20{col 46}{space 3}0.231{col 54}{space 4}-.6147584{col 67}{space 3} .1483687
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.2026058{col 26}{space 2} .1113188{col 37}{space 1}   -1.82{col 46}{space 3}0.069{col 54}{space 4}-.4207866{col 67}{space 3} .0155749
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1720169{col 26}{space 2} .0580965{col 37}{space 1}   -2.96{col 46}{space 3}0.003{col 54}{space 4}-.2858839{col 67}{space 3}-.0581498
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.1414279{col 26}{space 2} .1041627{col 37}{space 1}   -1.36{col 46}{space 3}0.175{col 54}{space 4} -.345583{col 67}{space 3} .0627273
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.1108389{col 26}{space 2} .1865901{col 37}{space 1}   -0.59{col 46}{space 3}0.552{col 54}{space 4}-.4765487{col 67}{space 3}  .254871
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) ///
>                 xtitle("Prevalence of Vote Buying, Vdem") ///
>                 ytitle(Effects on Trust in Elections) ///
>                 title(Indirect Exposure to Vote Buying)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:vbuy}{p_end}
{res}{txt}
{com}.         graph save Graph "fig1vbuy_indirect.gph", replace                               
{res}{txt}file {bf:fig1vbuy_indirect.gph} saved

{com}. mixed trustel i.direct##c.vbuy woman quintall agecohort edr rural m1 if wave==2014 [pweight = weight1500] || pais: 
{res}
{txt}Obtaining starting values by EM ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res: -58100.39}  
Iteration 1:{space 2}Log pseudolikelihood = {res: -58100.39}  (backed up)
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 53}Number of obs{col 69} = {res} 31,280
{txt}Group variable: {res}pais{col 53}{txt}Number of groups{col 69} = {res}     21
{txt}{col 53}Obs per group:
{col 66}min = {res}  1,285
{txt}{col 66}avg = {res}1,489.5
{txt}{col 66}max = {res}  2,862
{col 53}{txt}Wald chi2({res}9{txt}){col 69} = {res} 735.69
{txt}Log pseudolikelihood = {res} -58100.39{col 53}{txt}Prob > chi2{col 69} = {res} 0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:21} clusters in {res:pais})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}      trustel{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}1.direct {c |}{col 15}{res}{space 2}-.0750175{col 27}{space 2} .3010801{col 38}{space 1}   -0.25{col 47}{space 3}0.803{col 55}{space 4}-.6651237{col 68}{space 3} .5150887
{txt}{space 9}vbuy {c |}{col 15}{res}{space 2} -2.14524{col 27}{space 2} .3457489{col 38}{space 1}   -6.20{col 47}{space 3}0.000{col 55}{space 4}-2.822895{col 68}{space 3}-1.467585
{txt}{space 13} {c |}
direct#c.vbuy {c |}
{space 11}1  {c |}{col 15}{res}{space 2}-.1045403{col 27}{space 2} .5067563{col 38}{space 1}   -0.21{col 47}{space 3}0.837{col 55}{space 4}-1.097764{col 68}{space 3} .8886839
{txt}{space 13} {c |}
{space 8}woman {c |}{col 15}{res}{space 2}-.0614717{col 27}{space 2} .0181841{col 38}{space 1}   -3.38{col 47}{space 3}0.001{col 55}{space 4}-.0971119{col 68}{space 3}-.0258316
{txt}{space 5}quintall {c |}{col 15}{res}{space 2}-.0071316{col 27}{space 2} .0501049{col 38}{space 1}   -0.14{col 47}{space 3}0.887{col 55}{space 4}-.1053354{col 68}{space 3} .0910723
{txt}{space 4}agecohort {c |}{col 15}{res}{space 2} .2753093{col 27}{space 2} .0929714{col 38}{space 1}    2.96{col 47}{space 3}0.003{col 55}{space 4} .0930887{col 68}{space 3} .4575299
{txt}{space 10}edr {c |}{col 15}{res}{space 2} .1056498{col 27}{space 2} .0979135{col 38}{space 1}    1.08{col 47}{space 3}0.281{col 55}{space 4}-.0862571{col 68}{space 3} .2975567
{txt}{space 8}rural {c |}{col 15}{res}{space 2} .1734712{col 27}{space 2} .0527452{col 38}{space 1}    3.29{col 47}{space 3}0.001{col 55}{space 4} .0700925{col 68}{space 3} .2768499
{txt}{space 11}m1 {c |}{col 15}{res}{space 2} 2.612609{col 27}{space 2} .2369638{col 38}{space 1}   11.03{col 47}{space 3}0.000{col 55}{space 4} 2.148169{col 68}{space 3}  3.07705
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 3.432855{col 27}{space 2}  .236974{col 38}{space 1}   14.49{col 47}{space 3}0.000{col 55}{space 4} 2.968395{col 68}{space 3} 3.897316
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}pais{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33} .1980959{col 44}  .054911{col 58} .1150609{col 70} .3410542
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 3.010799{col 44} .1045085{col 58} 2.812779{col 70} 3.222761
{txt}{hline 29}{c BT}{hline 48}

{p 0 9 2}Warning: Sampling weights were 
specified only at the first level 
in a multilevel model. If these weights are 
indicative of overall and not conditional 
inclusion probabilities, then 
{help mixed##sampling:results may be biased}.
{p_end}

{com}.         outreg2 using a19.doc, dec(2) append ctitle(Direct)
{txt}{stata `"shellout using `"a19.doc"'"':a19.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a19.txt""':seeout}

{com}.         margins, dydx(direct) at(vbuy=(0(.25)1)) 
{res}
{txt}{col 1}Average marginal effects{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:31,280}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.direct}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 4:vbuy} = {res:{ralign 3:0}}
{lalign 7:2._at: }{space 0}{lalign 4:vbuy} = {res:{ralign 3:.25}}
{lalign 7:3._at: }{space 0}{lalign 4:vbuy} = {res:{ralign 3:.5}}
{lalign 7:4._at: }{space 0}{lalign 4:vbuy} = {res:{ralign 3:.75}}
{lalign 7:5._at: }{space 0}{lalign 4:vbuy} = {res:{ralign 3:1}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.direct    {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.direct     {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0750175{col 26}{space 2} .3010801{col 37}{space 1}   -0.25{col 46}{space 3}0.803{col 54}{space 4}-.6651237{col 67}{space 3} .5150887
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.1011526{col 26}{space 2} .1814297{col 37}{space 1}   -0.56{col 46}{space 3}0.577{col 54}{space 4}-.4567483{col 67}{space 3} .2544432
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1272876{col 26}{space 2} .0853493{col 37}{space 1}   -1.49{col 46}{space 3}0.136{col 54}{space 4}-.2945693{col 67}{space 3}  .039994
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.1534227{col 26}{space 2} .1172711{col 37}{space 1}   -1.31{col 46}{space 3}0.191{col 54}{space 4}-.3832699{col 67}{space 3} .0764244
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.1795578{col 26}{space 2} .2287373{col 37}{space 1}   -0.78{col 46}{space 3}0.432{col 54}{space 4}-.6278747{col 67}{space 3} .2687591
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) ///
>                 xtitle("Prevalence of Vote Buying, Vdem") ///
>                 ytitle(Effects on Trust in Elections) ///
>                 title(Direct Exposure to Vote Buying)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:vbuy}{p_end}
{res}{txt}
{com}.         graph save Graph "fig1vbuy_direct.gph", replace
{res}{txt}file {bf:fig1vbuy_direct.gph} saved

{com}. graph combine fig1vbuy_direct.gph fig1vbuy_indirect.gph
{res}{txt}
{com}.         graph save Graph "a19.gph", replace
{res}{txt}file {bf:a19.gph} saved

{com}.         
.         
. ***A21: predictors of norms (AB 2018) // results don't change if using idio2 and soct2 as categorical variables in its original scale
. svy: reg approval woman quintall agecohort edr rural i.pais if wave==2018
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 3:35}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:10,084}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 3:562}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:9,813.2715}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:527}
{txt}{col 51}{lalign 15:F({res:11}, {res:517})}{col 66} = {res}{ralign 10:49.22}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0577}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}           approval{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}woman {c |}{col 21}{res}{space 2}-.0235753{col 33}{space 2} .0056752{col 44}{space 1}   -4.15{col 53}{space 3}0.000{col 61}{space 4} -.034724{col 74}{space 3}-.0124266
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}-.0447663{col 33}{space 2} .0082114{col 44}{space 1}   -5.45{col 53}{space 3}0.000{col 61}{space 4}-.0608974{col 74}{space 3}-.0286352
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2}-.1107215{col 33}{space 2} .0089622{col 44}{space 1}  -12.35{col 53}{space 3}0.000{col 61}{space 4}-.1283276{col 74}{space 3}-.0931154
{txt}{space 16}edr {c |}{col 21}{res}{space 2}-.1232253{col 33}{space 2} .0132141{col 44}{space 1}   -9.33{col 53}{space 3}0.000{col 61}{space 4} -.149184{col 74}{space 3}-.0972666
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .0289952{col 33}{space 2} .0064184{col 44}{space 1}    4.52{col 53}{space 3}0.000{col 61}{space 4} .0163865{col 74}{space 3} .0416039
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2} .0399042{col 33}{space 2} .0100181{col 44}{space 1}    3.98{col 53}{space 3}0.000{col 61}{space 4} .0202238{col 74}{space 3} .0595846
{txt}{space 9}Nicaragua  {c |}{col 21}{res}{space 2} .0311925{col 33}{space 2} .0104561{col 44}{space 1}    2.98{col 53}{space 3}0.003{col 61}{space 4} .0106517{col 74}{space 3} .0517333
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2} .0046283{col 33}{space 2}  .009571{col 44}{space 1}    0.48{col 53}{space 3}0.629{col 61}{space 4}-.0141738{col 74}{space 3} .0234304
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2} .0473779{col 33}{space 2} .0100961{col 44}{space 1}    4.69{col 53}{space 3}0.000{col 61}{space 4} .0275443{col 74}{space 3} .0672116
{txt}Dominican Republic  {c |}{col 21}{res}{space 2}  .130864{col 33}{space 2} .0114665{col 44}{space 1}   11.41{col 53}{space 3}0.000{col 61}{space 4} .1083384{col 74}{space 3} .1533895
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2} .0769346{col 33}{space 2} .0117213{col 44}{space 1}    6.56{col 53}{space 3}0.000{col 61}{space 4} .0539083{col 74}{space 3} .0999609
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} .4275441{col 33}{space 2} .0130884{col 44}{space 1}   32.67{col 53}{space 3}0.000{col 61}{space 4} .4018322{col 74}{space 3}  .453256
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using a21_1.doc, dec(2) replace ctitle(Demographics) drop(i.pais)
{txt}{stata `"shellout using `"a21_1.doc"'"':a21_1.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a21_1.txt""':seeout}

{com}. svy: reg approval woman quintall agecohort edr rural m1 ing4 i.pais if wave==2018
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 3:35}{txt}{col 52}{lalign 15:Number of obs}{col 67} = {res}{ralign 9:9,608}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 3:562}{txt}{col 52}{lalign 15:Population size}{col 67} = {res}{ralign 9:9,349.575}
{txt}{col 52}{lalign 15:Design df}{col 67} = {res}{ralign 9:527}
{txt}{col 52}{lalign 15:F({res:13}, {res:515})}{col 67} = {res}{ralign 9:42.54}
{txt}{col 52}{lalign 15:Prob > F}{col 67} = {res}{ralign 9:0.0000}
{txt}{col 52}{lalign 15:R-squared}{col 67} = {res}{ralign 9:0.0618}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}           approval{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}woman {c |}{col 21}{res}{space 2}-.0230599{col 33}{space 2}  .005796{col 44}{space 1}   -3.98{col 53}{space 3}0.000{col 61}{space 4}-.0344461{col 74}{space 3}-.0116737
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}-.0426203{col 33}{space 2} .0085534{col 44}{space 1}   -4.98{col 53}{space 3}0.000{col 61}{space 4}-.0594232{col 74}{space 3}-.0258173
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2}-.1112718{col 33}{space 2} .0092403{col 44}{space 1}  -12.04{col 53}{space 3}0.000{col 61}{space 4}-.1294242{col 74}{space 3}-.0931194
{txt}{space 16}edr {c |}{col 21}{res}{space 2}-.1223497{col 33}{space 2} .0136394{col 44}{space 1}   -8.97{col 53}{space 3}0.000{col 61}{space 4}-.1491439{col 74}{space 3}-.0955555
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .0248235{col 33}{space 2} .0066617{col 44}{space 1}    3.73{col 53}{space 3}0.000{col 61}{space 4} .0117366{col 74}{space 3} .0379103
{txt}{space 17}m1 {c |}{col 21}{res}{space 2} .0648653{col 33}{space 2} .0114346{col 44}{space 1}    5.67{col 53}{space 3}0.000{col 61}{space 4} .0424023{col 74}{space 3} .0873283
{txt}{space 15}ing4 {c |}{col 21}{res}{space 2}-.0201934{col 33}{space 2} .0096239{col 44}{space 1}   -2.10{col 53}{space 3}0.036{col 61}{space 4}-.0390994{col 74}{space 3}-.0012875
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2} .0623884{col 33}{space 2} .0106351{col 44}{space 1}    5.87{col 53}{space 3}0.000{col 61}{space 4}  .041496{col 74}{space 3} .0832807
{txt}{space 9}Nicaragua  {c |}{col 21}{res}{space 2} .0460552{col 33}{space 2}  .011248{col 44}{space 1}    4.09{col 53}{space 3}0.000{col 61}{space 4} .0239587{col 74}{space 3} .0681516
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2} .0135182{col 33}{space 2} .0099344{col 44}{space 1}    1.36{col 53}{space 3}0.174{col 61}{space 4}-.0059977{col 74}{space 3} .0330341
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2} .0570833{col 33}{space 2} .0103553{col 44}{space 1}    5.51{col 53}{space 3}0.000{col 61}{space 4} .0367405{col 74}{space 3} .0774261
{txt}Dominican Republic  {c |}{col 21}{res}{space 2} .1417894{col 33}{space 2} .0116044{col 44}{space 1}   12.22{col 53}{space 3}0.000{col 61}{space 4} .1189928{col 74}{space 3}  .164586
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2}  .085144{col 33}{space 2} .0122412{col 44}{space 1}    6.96{col 53}{space 3}0.000{col 61}{space 4} .0610964{col 74}{space 3} .1091915
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} .3919644{col 33}{space 2} .0160445{col 44}{space 1}   24.43{col 53}{space 3}0.000{col 61}{space 4} .3604454{col 74}{space 3} .4234834
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using a21_1.doc, dec(2) append ctitle(+m1 and ing4) drop(i.pais)
{txt}{stata `"shellout using `"a21_1.doc"'"':a21_1.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a21_1.txt""':seeout}

{com}. svy: reg approval woman quintall agecohort edr rural m1 ing4 idio2 soct2 exc7new i.pais if wave==2018
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 3:35}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:5,177}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 3:561}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:5,047.8601}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:526}
{txt}{col 51}{lalign 15:F({res:16}, {res:511})}{col 66} = {res}{ralign 10:22.90}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0657}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}           approval{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}woman {c |}{col 21}{res}{space 2}-.0161881{col 33}{space 2} .0081223{col 44}{space 1}   -1.99{col 53}{space 3}0.047{col 61}{space 4}-.0321442{col 74}{space 3} -.000232
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}-.0351344{col 33}{space 2} .0119381{col 44}{space 1}   -2.94{col 53}{space 3}0.003{col 61}{space 4}-.0585866{col 74}{space 3}-.0116823
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2}-.1251102{col 33}{space 2} .0123921{col 44}{space 1}  -10.10{col 53}{space 3}0.000{col 61}{space 4}-.1494543{col 74}{space 3} -.100766
{txt}{space 16}edr {c |}{col 21}{res}{space 2}-.1232328{col 33}{space 2} .0183429{col 44}{space 1}   -6.72{col 53}{space 3}0.000{col 61}{space 4}-.1592672{col 74}{space 3}-.0871985
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .0213159{col 33}{space 2} .0090269{col 44}{space 1}    2.36{col 53}{space 3}0.019{col 61}{space 4} .0035828{col 74}{space 3} .0390491
{txt}{space 17}m1 {c |}{col 21}{res}{space 2} .0810952{col 33}{space 2} .0169581{col 44}{space 1}    4.78{col 53}{space 3}0.000{col 61}{space 4} .0477813{col 74}{space 3} .1144092
{txt}{space 15}ing4 {c |}{col 21}{res}{space 2}-.0039047{col 33}{space 2} .0138385{col 44}{space 1}   -0.28{col 53}{space 3}0.778{col 61}{space 4}-.0310901{col 74}{space 3} .0232807
{txt}{space 14}idio2 {c |}{col 21}{res}{space 2} .0133575{col 33}{space 2} .0128366{col 44}{space 1}    1.04{col 53}{space 3}0.299{col 61}{space 4}-.0118599{col 74}{space 3} .0385748
{txt}{space 14}soct2 {c |}{col 21}{res}{space 2}-.0044661{col 33}{space 2} .0136559{col 44}{space 1}   -0.33{col 53}{space 3}0.744{col 61}{space 4}-.0312928{col 74}{space 3} .0223607
{txt}{space 12}exc7new {c |}{col 21}{res}{space 2}-.0253679{col 33}{space 2} .0154188{col 44}{space 1}   -1.65{col 53}{space 3}0.101{col 61}{space 4}-.0556578{col 74}{space 3}  .004922
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2} .0730991{col 33}{space 2} .0158724{col 44}{space 1}    4.61{col 53}{space 3}0.000{col 61}{space 4} .0419179{col 74}{space 3} .1042802
{txt}{space 9}Nicaragua  {c |}{col 21}{res}{space 2} .0582925{col 33}{space 2} .0158152{col 44}{space 1}    3.69{col 53}{space 3}0.000{col 61}{space 4} .0272239{col 74}{space 3} .0893611
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2} .0203303{col 33}{space 2} .0137114{col 44}{space 1}    1.48{col 53}{space 3}0.139{col 61}{space 4}-.0066055{col 74}{space 3} .0472661
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2} .0658627{col 33}{space 2} .0129582{col 44}{space 1}    5.08{col 53}{space 3}0.000{col 61}{space 4} .0404066{col 74}{space 3} .0913189
{txt}Dominican Republic  {c |}{col 21}{res}{space 2} .1603862{col 33}{space 2} .0165723{col 44}{space 1}    9.68{col 53}{space 3}0.000{col 61}{space 4} .1278301{col 74}{space 3} .1929422
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2} .0948463{col 33}{space 2} .0176437{col 44}{space 1}    5.38{col 53}{space 3}0.000{col 61}{space 4} .0601856{col 74}{space 3} .1295071
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} .3783887{col 33}{space 2}  .026155{col 44}{space 1}   14.47{col 53}{space 3}0.000{col 61}{space 4} .3270076{col 74}{space 3} .4297698
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using a21_1.doc, dec(2) append ctitle(+econ and corrupt) drop(i.pais)
{txt}{stata `"shellout using `"a21_1.doc"'"':a21_1.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a21_1.txt""':seeout}

{com}. svy: reg approval woman quintall agecohort edr rural m1 ing4 idio2 soct2 exc7new psa5 i.pais if wave==2018
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 3:35}{txt}{col 52}{lalign 15:Number of obs}{col 67} = {res}{ralign 9:5,155}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 3:561}{txt}{col 52}{lalign 15:Population size}{col 67} = {res}{ralign 9:5,026.595}
{txt}{col 52}{lalign 15:Design df}{col 67} = {res}{ralign 9:526}
{txt}{col 52}{lalign 15:F({res:17}, {res:510})}{col 67} = {res}{ralign 9:22.37}
{txt}{col 52}{lalign 15:Prob > F}{col 67} = {res}{ralign 9:0.0000}
{txt}{col 52}{lalign 15:R-squared}{col 67} = {res}{ralign 9:0.0676}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}           approval{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}woman {c |}{col 21}{res}{space 2}-.0169043{col 33}{space 2} .0081136{col 44}{space 1}   -2.08{col 53}{space 3}0.038{col 61}{space 4}-.0328434{col 74}{space 3}-.0009653
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}  -.03229{col 33}{space 2} .0120632{col 44}{space 1}   -2.68{col 53}{space 3}0.008{col 61}{space 4}-.0559879{col 74}{space 3}-.0085921
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2}-.1227089{col 33}{space 2} .0123899{col 44}{space 1}   -9.90{col 53}{space 3}0.000{col 61}{space 4}-.1470487{col 74}{space 3}-.0983691
{txt}{space 16}edr {c |}{col 21}{res}{space 2}-.1197201{col 33}{space 2} .0185107{col 44}{space 1}   -6.47{col 53}{space 3}0.000{col 61}{space 4}-.1560841{col 74}{space 3}-.0833561
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .0189989{col 33}{space 2} .0090946{col 44}{space 1}    2.09{col 53}{space 3}0.037{col 61}{space 4} .0011327{col 74}{space 3}  .036865
{txt}{space 17}m1 {c |}{col 21}{res}{space 2} .0647103{col 33}{space 2} .0176213{col 44}{space 1}    3.67{col 53}{space 3}0.000{col 61}{space 4} .0300934{col 74}{space 3} .0993271
{txt}{space 15}ing4 {c |}{col 21}{res}{space 2}-.0124651{col 33}{space 2} .0141779{col 44}{space 1}   -0.88{col 53}{space 3}0.380{col 61}{space 4}-.0403175{col 74}{space 3} .0153872
{txt}{space 14}idio2 {c |}{col 21}{res}{space 2}  .016083{col 33}{space 2}  .012839{col 44}{space 1}    1.25{col 53}{space 3}0.211{col 61}{space 4} -.009139{col 74}{space 3} .0413049
{txt}{space 14}soct2 {c |}{col 21}{res}{space 2}-.0037889{col 33}{space 2} .0137086{col 44}{space 1}   -0.28{col 53}{space 3}0.782{col 61}{space 4}-.0307193{col 74}{space 3} .0231414
{txt}{space 12}exc7new {c |}{col 21}{res}{space 2}-.0140843{col 33}{space 2} .0157541{col 44}{space 1}   -0.89{col 53}{space 3}0.372{col 61}{space 4} -.045033{col 74}{space 3} .0168643
{txt}{space 15}psa5 {c |}{col 21}{res}{space 2} .0568345{col 33}{space 2}  .019382{col 44}{space 1}    2.93{col 53}{space 3}0.004{col 61}{space 4} .0187589{col 74}{space 3} .0949102
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2} .0702879{col 33}{space 2} .0157411{col 44}{space 1}    4.47{col 53}{space 3}0.000{col 61}{space 4} .0393646{col 74}{space 3} .1012111
{txt}{space 9}Nicaragua  {c |}{col 21}{res}{space 2} .0556019{col 33}{space 2} .0156902{col 44}{space 1}    3.54{col 53}{space 3}0.000{col 61}{space 4} .0247787{col 74}{space 3} .0864251
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2}  .023433{col 33}{space 2} .0136794{col 44}{space 1}    1.71{col 53}{space 3}0.087{col 61}{space 4}-.0034398{col 74}{space 3} .0503059
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2} .0697285{col 33}{space 2} .0129242{col 44}{space 1}    5.40{col 53}{space 3}0.000{col 61}{space 4}  .044339{col 74}{space 3}  .095118
{txt}Dominican Republic  {c |}{col 21}{res}{space 2}   .16336{col 33}{space 2}    .0165{col 44}{space 1}    9.90{col 53}{space 3}0.000{col 61}{space 4} .1309461{col 74}{space 3} .1957739
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2} .0963482{col 33}{space 2}   .01745{col 44}{space 1}    5.52{col 53}{space 3}0.000{col 61}{space 4} .0620678{col 74}{space 3} .1306285
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} .3506222{col 33}{space 2}  .027962{col 44}{space 1}   12.54{col 53}{space 3}0.000{col 61}{space 4} .2956914{col 74}{space 3} .4055531
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using a21_1.doc, dec(2) append ctitle(Full) drop(i.pais)
{txt}{stata `"shellout using `"a21_1.doc"'"':a21_1.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a21_1.txt""':seeout}

{com}. 
. gen winner=.
{txt}(310,254 missing values generated)

{com}. replace winner=0 if !missing(vb3n_18) // voted but lost
{txt}(18,399 real changes made)

{com}. replace winner=0 if vb2==2 // non-voters
{txt}(73,909 real changes made)

{com}. replace winner=1 if pais==1 & vb3n_18==101
{txt}(786 real changes made)

{com}. replace winner=1 if pais==2 & vb3n_18==201
{txt}(362 real changes made)

{com}. replace winner=1 if pais==11 & vb3n_18==1101
{txt}(486 real changes made)

{com}. replace winner=1 if pais==12 & vb3n_18==1201
{txt}(626 real changes made)

{com}. replace winner=1 if pais==21 & vb3n_18==2101
{txt}(720 real changes made)

{com}. replace winner=1 if pais==23 & vb3n_18==2302
{txt}(346 real changes made)

{com}.         
. svy: reg approval woman quintall agecohort edr rural i.pais if wave==2018
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 3:35}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:10,084}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 3:562}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:9,813.2715}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:527}
{txt}{col 51}{lalign 15:F({res:11}, {res:517})}{col 66} = {res}{ralign 10:49.22}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0577}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}           approval{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}woman {c |}{col 21}{res}{space 2}-.0235753{col 33}{space 2} .0056752{col 44}{space 1}   -4.15{col 53}{space 3}0.000{col 61}{space 4} -.034724{col 74}{space 3}-.0124266
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}-.0447663{col 33}{space 2} .0082114{col 44}{space 1}   -5.45{col 53}{space 3}0.000{col 61}{space 4}-.0608974{col 74}{space 3}-.0286352
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2}-.1107215{col 33}{space 2} .0089622{col 44}{space 1}  -12.35{col 53}{space 3}0.000{col 61}{space 4}-.1283276{col 74}{space 3}-.0931154
{txt}{space 16}edr {c |}{col 21}{res}{space 2}-.1232253{col 33}{space 2} .0132141{col 44}{space 1}   -9.33{col 53}{space 3}0.000{col 61}{space 4} -.149184{col 74}{space 3}-.0972666
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .0289952{col 33}{space 2} .0064184{col 44}{space 1}    4.52{col 53}{space 3}0.000{col 61}{space 4} .0163865{col 74}{space 3} .0416039
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2} .0399042{col 33}{space 2} .0100181{col 44}{space 1}    3.98{col 53}{space 3}0.000{col 61}{space 4} .0202238{col 74}{space 3} .0595846
{txt}{space 9}Nicaragua  {c |}{col 21}{res}{space 2} .0311925{col 33}{space 2} .0104561{col 44}{space 1}    2.98{col 53}{space 3}0.003{col 61}{space 4} .0106517{col 74}{space 3} .0517333
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2} .0046283{col 33}{space 2}  .009571{col 44}{space 1}    0.48{col 53}{space 3}0.629{col 61}{space 4}-.0141738{col 74}{space 3} .0234304
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2} .0473779{col 33}{space 2} .0100961{col 44}{space 1}    4.69{col 53}{space 3}0.000{col 61}{space 4} .0275443{col 74}{space 3} .0672116
{txt}Dominican Republic  {c |}{col 21}{res}{space 2}  .130864{col 33}{space 2} .0114665{col 44}{space 1}   11.41{col 53}{space 3}0.000{col 61}{space 4} .1083384{col 74}{space 3} .1533895
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2} .0769346{col 33}{space 2} .0117213{col 44}{space 1}    6.56{col 53}{space 3}0.000{col 61}{space 4} .0539083{col 74}{space 3} .0999609
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} .4275441{col 33}{space 2} .0130884{col 44}{space 1}   32.67{col 53}{space 3}0.000{col 61}{space 4} .4018322{col 74}{space 3}  .453256
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using a21_2.doc, dec(2) replace ctitle(Demographics) drop(i.pais)
{txt}{stata `"shellout using `"a21_2.doc"'"':a21_2.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a21_2.txt""':seeout}

{com}. svy: reg approval woman quintall agecohort edr rural winner ing4 i.pais if wave==2018
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 3:35}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:8,360}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 3:562}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:8,140.4085}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:527}
{txt}{col 51}{lalign 15:F({res:13}, {res:515})}{col 66} = {res}{ralign 10:35.20}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0607}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}           approval{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}woman {c |}{col 21}{res}{space 2}-.0255361{col 33}{space 2} .0062023{col 44}{space 1}   -4.12{col 53}{space 3}0.000{col 61}{space 4}-.0377204{col 74}{space 3}-.0133517
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}-.0406466{col 33}{space 2} .0094786{col 44}{space 1}   -4.29{col 53}{space 3}0.000{col 61}{space 4} -.059267{col 74}{space 3}-.0220262
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2}-.1154341{col 33}{space 2} .0101931{col 44}{space 1}  -11.32{col 53}{space 3}0.000{col 61}{space 4}-.1354582{col 74}{space 3}  -.09541
{txt}{space 16}edr {c |}{col 21}{res}{space 2}-.1301305{col 33}{space 2} .0147861{col 44}{space 1}   -8.80{col 53}{space 3}0.000{col 61}{space 4}-.1591774{col 74}{space 3}-.1010836
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .0259204{col 33}{space 2} .0069665{col 44}{space 1}    3.72{col 53}{space 3}0.000{col 61}{space 4} .0122349{col 74}{space 3} .0396059
{txt}{space 13}winner {c |}{col 21}{res}{space 2} .0157987{col 33}{space 2} .0066386{col 44}{space 1}    2.38{col 53}{space 3}0.018{col 61}{space 4} .0027573{col 74}{space 3} .0288401
{txt}{space 15}ing4 {c |}{col 21}{res}{space 2}-.0168203{col 33}{space 2} .0105939{col 44}{space 1}   -1.59{col 53}{space 3}0.113{col 61}{space 4}-.0376318{col 74}{space 3} .0039912
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2} .0376076{col 33}{space 2} .0119174{col 44}{space 1}    3.16{col 53}{space 3}0.002{col 61}{space 4} .0141962{col 74}{space 3} .0610191
{txt}{space 9}Nicaragua  {c |}{col 21}{res}{space 2} .0427708{col 33}{space 2} .0116947{col 44}{space 1}    3.66{col 53}{space 3}0.000{col 61}{space 4} .0197968{col 74}{space 3} .0657448
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2} .0050479{col 33}{space 2} .0106849{col 44}{space 1}    0.47{col 53}{space 3}0.637{col 61}{space 4}-.0159423{col 74}{space 3} .0260382
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2} .0494016{col 33}{space 2} .0108304{col 44}{space 1}    4.56{col 53}{space 3}0.000{col 61}{space 4} .0281256{col 74}{space 3} .0706776
{txt}Dominican Republic  {c |}{col 21}{res}{space 2} .1355888{col 33}{space 2} .0124982{col 44}{space 1}   10.85{col 53}{space 3}0.000{col 61}{space 4} .1110364{col 74}{space 3} .1601411
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2} .0848408{col 33}{space 2} .0134222{col 44}{space 1}    6.32{col 53}{space 3}0.000{col 61}{space 4} .0584733{col 74}{space 3} .1112083
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} .4356997{col 33}{space 2}  .015583{col 44}{space 1}   27.96{col 53}{space 3}0.000{col 61}{space 4} .4050873{col 74}{space 3}  .466312
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using a21_2.doc, dec(2) append ctitle(+winner and ing4) drop(i.pais)
{txt}{stata `"shellout using `"a21_2.doc"'"':a21_2.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a21_2.txt""':seeout}

{com}. svy: reg approval woman quintall agecohort edr rural winner ing4 idio2 soct2 exc7new i.pais if wave==2018
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 3:35}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:4,532}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 3:560}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:4,420.5306}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:525}
{txt}{col 51}{lalign 15:F({res:16}, {res:510})}{col 66} = {res}{ralign 10:17.66}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0608}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}           approval{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}woman {c |}{col 21}{res}{space 2}-.0185938{col 33}{space 2} .0086448{col 44}{space 1}   -2.15{col 53}{space 3}0.032{col 61}{space 4}-.0355765{col 74}{space 3}-.0016111
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}-.0329746{col 33}{space 2} .0132417{col 44}{space 1}   -2.49{col 53}{space 3}0.013{col 61}{space 4}-.0589879{col 74}{space 3}-.0069614
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2}-.1236315{col 33}{space 2} .0134605{col 44}{space 1}   -9.18{col 53}{space 3}0.000{col 61}{space 4}-.1500746{col 74}{space 3}-.0971884
{txt}{space 16}edr {c |}{col 21}{res}{space 2}-.1219585{col 33}{space 2} .0200898{col 44}{space 1}   -6.07{col 53}{space 3}0.000{col 61}{space 4}-.1614248{col 74}{space 3}-.0824921
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .0227571{col 33}{space 2} .0096554{col 44}{space 1}    2.36{col 53}{space 3}0.019{col 61}{space 4} .0037892{col 74}{space 3} .0417251
{txt}{space 13}winner {c |}{col 21}{res}{space 2} .0059421{col 33}{space 2}   .00865{col 44}{space 1}    0.69{col 53}{space 3}0.492{col 61}{space 4}-.0110508{col 74}{space 3} .0229349
{txt}{space 15}ing4 {c |}{col 21}{res}{space 2}-.0024756{col 33}{space 2} .0150616{col 44}{space 1}   -0.16{col 53}{space 3}0.870{col 61}{space 4} -.032064{col 74}{space 3} .0271129
{txt}{space 14}idio2 {c |}{col 21}{res}{space 2} .0073444{col 33}{space 2}  .014249{col 44}{space 1}    0.52{col 53}{space 3}0.606{col 61}{space 4}-.0206477{col 74}{space 3} .0353365
{txt}{space 14}soct2 {c |}{col 21}{res}{space 2}-.0089263{col 33}{space 2} .0147049{col 44}{space 1}   -0.61{col 53}{space 3}0.544{col 61}{space 4}-.0378139{col 74}{space 3} .0199614
{txt}{space 12}exc7new {c |}{col 21}{res}{space 2}-.0485808{col 33}{space 2} .0168137{col 44}{space 1}   -2.89{col 53}{space 3}0.004{col 61}{space 4}-.0816112{col 74}{space 3}-.0155505
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2} .0444477{col 33}{space 2} .0182406{col 44}{space 1}    2.44{col 53}{space 3}0.015{col 61}{space 4} .0086143{col 74}{space 3} .0802812
{txt}{space 9}Nicaragua  {c |}{col 21}{res}{space 2} .0462473{col 33}{space 2}  .017275{col 44}{space 1}    2.68{col 53}{space 3}0.008{col 61}{space 4} .0123108{col 74}{space 3} .0801839
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2}  .006993{col 33}{space 2} .0155001{col 44}{space 1}    0.45{col 53}{space 3}0.652{col 61}{space 4}-.0234569{col 74}{space 3} .0374429
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2} .0526432{col 33}{space 2} .0141619{col 44}{space 1}    3.72{col 53}{space 3}0.000{col 61}{space 4} .0248224{col 74}{space 3} .0804641
{txt}Dominican Republic  {c |}{col 21}{res}{space 2} .1467903{col 33}{space 2} .0188262{col 44}{space 1}    7.80{col 53}{space 3}0.000{col 61}{space 4} .1098063{col 74}{space 3} .1837743
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2} .0863466{col 33}{space 2} .0189381{col 44}{space 1}    4.56{col 53}{space 3}0.000{col 61}{space 4} .0491429{col 74}{space 3} .1235503
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} .4583103{col 33}{space 2} .0246243{col 44}{space 1}   18.61{col 53}{space 3}0.000{col 61}{space 4} .4099361{col 74}{space 3} .5066846
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using a21_2.doc, dec(2) append ctitle(+econ and corrupt) drop(i.pais)
{txt}{stata `"shellout using `"a21_2.doc"'"':a21_2.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a21_2.txt""':seeout}

{com}. svy: reg approval woman quintall agecohort edr rural winner ing4 idio2 soct2 exc7new psa5 i.pais if wave==2018
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 3:35}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:4,517}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 3:560}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:4,405.9708}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:525}
{txt}{col 51}{lalign 15:F({res:17}, {res:509})}{col 66} = {res}{ralign 10:17.89}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0646}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}           approval{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}woman {c |}{col 21}{res}{space 2}-.0196533{col 33}{space 2} .0086819{col 44}{space 1}   -2.26{col 53}{space 3}0.024{col 61}{space 4}-.0367089{col 74}{space 3}-.0025977
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}-.0278766{col 33}{space 2} .0132998{col 44}{space 1}   -2.10{col 53}{space 3}0.037{col 61}{space 4}-.0540041{col 74}{space 3}-.0017492
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2}-.1222258{col 33}{space 2} .0134114{col 44}{space 1}   -9.11{col 53}{space 3}0.000{col 61}{space 4}-.1485724{col 74}{space 3}-.0958792
{txt}{space 16}edr {c |}{col 21}{res}{space 2}-.1175668{col 33}{space 2} .0201911{col 44}{space 1}   -5.82{col 53}{space 3}0.000{col 61}{space 4} -.157232{col 74}{space 3}-.0779015
{txt}{space 14}rural {c |}{col 21}{res}{space 2}  .019146{col 33}{space 2} .0096635{col 44}{space 1}    1.98{col 53}{space 3}0.048{col 61}{space 4} .0001621{col 74}{space 3} .0381298
{txt}{space 13}winner {c |}{col 21}{res}{space 2} .0033537{col 33}{space 2} .0086725{col 44}{space 1}    0.39{col 53}{space 3}0.699{col 61}{space 4}-.0136834{col 74}{space 3} .0203907
{txt}{space 15}ing4 {c |}{col 21}{res}{space 2}-.0147004{col 33}{space 2} .0154482{col 44}{space 1}   -0.95{col 53}{space 3}0.342{col 61}{space 4}-.0450484{col 74}{space 3} .0156475
{txt}{space 14}idio2 {c |}{col 21}{res}{space 2} .0130216{col 33}{space 2} .0142613{col 44}{space 1}    0.91{col 53}{space 3}0.362{col 61}{space 4}-.0149947{col 74}{space 3} .0410379
{txt}{space 14}soct2 {c |}{col 21}{res}{space 2}-.0059508{col 33}{space 2} .0147807{col 44}{space 1}   -0.40{col 53}{space 3}0.687{col 61}{space 4}-.0349873{col 74}{space 3} .0230857
{txt}{space 12}exc7new {c |}{col 21}{res}{space 2}-.0295897{col 33}{space 2} .0172728{col 44}{space 1}   -1.71{col 53}{space 3}0.087{col 61}{space 4}-.0635219{col 74}{space 3} .0043425
{txt}{space 15}psa5 {c |}{col 21}{res}{space 2} .0750485{col 33}{space 2} .0194825{col 44}{space 1}    3.85{col 53}{space 3}0.000{col 61}{space 4} .0367754{col 74}{space 3} .1133217
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2} .0463628{col 33}{space 2} .0180331{col 44}{space 1}    2.57{col 53}{space 3}0.010{col 61}{space 4}  .010937{col 74}{space 3} .0817886
{txt}{space 9}Nicaragua  {c |}{col 21}{res}{space 2} .0461616{col 33}{space 2} .0171795{col 44}{space 1}    2.69{col 53}{space 3}0.007{col 61}{space 4} .0124126{col 74}{space 3} .0799105
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2} .0122799{col 33}{space 2} .0154302{col 44}{space 1}    0.80{col 53}{space 3}0.426{col 61}{space 4}-.0180325{col 74}{space 3} .0425924
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2} .0584159{col 33}{space 2}   .01416{col 44}{space 1}    4.13{col 53}{space 3}0.000{col 61}{space 4} .0305987{col 74}{space 3} .0862331
{txt}Dominican Republic  {c |}{col 21}{res}{space 2} .1531598{col 33}{space 2} .0187538{col 44}{space 1}    8.17{col 53}{space 3}0.000{col 61}{space 4} .1163181{col 74}{space 3} .1900014
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2} .0899504{col 33}{space 2} .0185903{col 44}{space 1}    4.84{col 53}{space 3}0.000{col 61}{space 4} .0534299{col 74}{space 3} .1264708
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} .4037468{col 33}{space 2} .0283817{col 44}{space 1}   14.23{col 53}{space 3}0.000{col 61}{space 4} .3479912{col 74}{space 3} .4595025
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using a21_2.doc, dec(2) append ctitle(Full) drop(i.pais)
{txt}{stata `"shellout using `"a21_2.doc"'"':a21_2.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a21_2.txt""':seeout}

{com}. 
. 
. ***A22: profile analysis based on A21 // re-load data set so variables are not rescaled to include non-integer values (for margins)
. preserve
{txt}
{com}.         use "../data/AB_cps.dta", clear
{txt}(All data are copyrighted by LAPOP. For more info, run the command note list)

{com}.         recode clien1 (1/2=1)(3=0), gen(client3)
{txt}(41,930 differences between {bf:clien1} and {bf:client3})

{com}.         recode clien1na (2=0), gen(client)
{txt}(47,955 differences between {bf:clien1na} and {bf:client})

{com}.         recode clien1n (2=0), gen(indirect)
{txt}(39,880 differences between {bf:clien1n} and {bf:indirect})

{com}.         egen direct = rowtotal(client3 client)
{txt}
{com}.                 replace direct=. if client>1 & client3>1
{txt}(212,829 real changes made, 212,829 to missing)

{com}.         egen trustel = rowtotal(b47a b47)
{txt}
{com}.                 replace trustel=. if missing(b47) & missing(b47a)
{txt}(56,106 real changes made, 56,106 to missing)

{com}.         egen approval=rowtotal(clien4 clien4a clien4b)
{txt}
{com}.                 sum clien4 clien4a clien4b

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}clien4 {c |}{res}      1,491    3.684105    1.012079          1          5
{txt}{space 5}clien4a {c |}{res}      4,465    3.705039    1.124282          1          5
{txt}{space 5}clien4b {c |}{res}      4,472    3.656753    1.130108          1          5
{txt}
{com}.                 tab approval // runs from strongly agree to strongly disagree

   {txt}approval {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}    299,826       96.64       96.64
{txt}          1 {c |}{res}        600        0.19       96.83
{txt}          2 {c |}{res}        897        0.29       97.12
{txt}          3 {c |}{res}      2,342        0.75       97.88
{txt}          4 {c |}{res}      3,976        1.28       99.16
{txt}          5 {c |}{res}      2,613        0.84      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}    310,254      100.00
{txt}
{com}.                 recode approval (0=.)
{txt}(299,826 changes made to {bf:approval})

{com}.                 rescale approval 1 0 // to run from strongly disagree(0) to strongly agree (1)
{txt}(9,828 real changes made)

{com}.         recode ur 1=0 2=1, gen(rural)
{txt}(290,476 differences between {bf:ur} and {bf:rural})

{com}.         recode q2 (16/25=0)(26/35=1)(36/45=2)(46/55=3)(56/65=4)(66/112=5), gen(agecohort)
{txt}(307,627 differences between {bf:q2} and {bf:agecohort})

{com}.                 fre agecohort
{res}
{txt}agecohort {hline 2} RECODE of q2 (Age)
{txt}{hline 13}{hline 1}{c TT}{hline 44}
{txt}        {txt}      {c |}      Freq.    Percent      Valid       Cum.
{txt}{hline 13}{hline 1}{c +}{hline 44}
{txt}Valid   0     {c |}{res}      69934      22.54      22.73      22.73
{txt}        1     {c |}{res}      72139      23.25      23.45      46.18
{txt}        2     {c |}{res}      60842      19.61      19.78      65.96
{txt}        3     {c |}{res}      47309      15.25      15.38      81.34
{txt}        4     {c |}{res}      32479      10.47      10.56      91.90
{txt}        5     {c |}{res}      24924       8.03       8.10     100.00
{txt}        Total {c |}{res}     307627      99.15     100.00           
{txt}Missing .     {c |}{res}         17       0.01                      
{txt}        .a    {c |}{res}        156       0.05                      
{txt}        .b    {c |}{res}        948       0.31                      
{txt}        .c    {c |}{res}          6       0.00                      
{txt}        .z    {c |}{res}       1500       0.48                      
{txt}        Total {c |}{res}       2627       0.85                      
{txt}Total         {c |}{res}     310254     100.00                      
{txt}{hline 13}{hline 1}{c BT}{hline 44}

{com}.         recode q1 (1=0)(2=1)(3=0), gen(woman)
{txt}(310,222 differences between {bf:q1} and {bf:woman})

{com}.                 tab woman q1, mis

 {txt}RECODE of {c |}                                     Sex
  q1 (Sex) {c |}      Male     Female      Other          .  Don't Kno  No Respon  Not Appli {c |}     Total
{hline 11}{c +}{hline 77}{c +}{hline 10}
         0 {c |}{res}   151,838          0         11          0          0          0          0 {txt}{c |}{res}   151,849 
{txt}         1 {c |}{res}         0    158,373          0          0          0          0          0 {txt}{c |}{res}   158,373 
{txt}         . {c |}{res}         0          0          0         15          0          0          0 {txt}{c |}{res}        15 
{txt}        .a {c |}{res}         0          0          0          0          3          0          0 {txt}{c |}{res}         3 
{txt}        .b {c |}{res}         0          0          0          0          0         13          0 {txt}{c |}{res}        13 
{txt}        .c {c |}{res}         0          0          0          0          0          0          1 {txt}{c |}{res}         1 
{txt}{hline 11}{c +}{hline 77}{c +}{hline 10}
     Total {c |}{res}   151,838    158,373         11         15          3         13          1 {txt}{c |}{res}   310,254 
{txt}
{com}. 
.         svy: reg approval i.woman i.quintall i.agecohort i.edr i.rural m1 ing4 i.idio2 i.soct2 exc7new psa5 i.pais
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 3:35}{txt}{col 52}{lalign 15:Number of obs}{col 67} = {res}{ralign 9:5,155}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 3:561}{txt}{col 52}{lalign 15:Population size}{col 67} = {res}{ralign 9:5,026.595}
{txt}{col 52}{lalign 15:Design df}{col 67} = {res}{ralign 9:526}
{txt}{col 52}{lalign 15:F({res:28}, {res:499})}{col 67} = {res}{ralign 9:15.69}
{txt}{col 52}{lalign 15:Prob > F}{col 67} = {res}{ralign 9:0.0000}
{txt}{col 52}{lalign 15:R-squared}{col 67} = {res}{ralign 9:0.0737}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}           approval{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}1.woman {c |}{col 21}{res}{space 2}-.0166827{col 33}{space 2} .0081008{col 44}{space 1}   -2.06{col 53}{space 3}0.040{col 61}{space 4}-.0325965{col 74}{space 3}-.0007689
{txt}{space 19} {c |}
{space 11}quintall {c |}
{space 17}2  {c |}{col 21}{res}{space 2} .0039735{col 33}{space 2} .0119986{col 44}{space 1}    0.33{col 53}{space 3}0.741{col 61}{space 4}-.0195976{col 74}{space 3} .0275445
{txt}{space 17}3  {c |}{col 21}{res}{space 2} .0031455{col 33}{space 2}  .011892{col 44}{space 1}    0.26{col 53}{space 3}0.791{col 61}{space 4}-.0202161{col 74}{space 3} .0265072
{txt}{space 17}4  {c |}{col 21}{res}{space 2}-.0058302{col 33}{space 2} .0119107{col 44}{space 1}   -0.49{col 53}{space 3}0.625{col 61}{space 4}-.0292285{col 74}{space 3} .0175682
{txt}{space 17}5  {c |}{col 21}{res}{space 2}-.0307559{col 33}{space 2}  .013556{col 44}{space 1}   -2.27{col 53}{space 3}0.024{col 61}{space 4}-.0573864{col 74}{space 3}-.0041254
{txt}{space 19} {c |}
{space 10}agecohort {c |}
{space 17}1  {c |}{col 21}{res}{space 2} .0082205{col 33}{space 2} .0110606{col 44}{space 1}    0.74{col 53}{space 3}0.458{col 61}{space 4}-.0135079{col 74}{space 3} .0299488
{txt}{space 17}2  {c |}{col 21}{res}{space 2}-.0371309{col 33}{space 2} .0113102{col 44}{space 1}   -3.28{col 53}{space 3}0.001{col 61}{space 4}-.0593495{col 74}{space 3}-.0149122
{txt}{space 17}3  {c |}{col 21}{res}{space 2}-.0773628{col 33}{space 2}  .012261{col 44}{space 1}   -6.31{col 53}{space 3}0.000{col 61}{space 4}-.1014494{col 74}{space 3}-.0532762
{txt}{space 17}4  {c |}{col 21}{res}{space 2}-.0814388{col 33}{space 2} .0146842{col 44}{space 1}   -5.55{col 53}{space 3}0.000{col 61}{space 4}-.1102857{col 74}{space 3}-.0525918
{txt}{space 17}5  {c |}{col 21}{res}{space 2}-.1085118{col 33}{space 2} .0154241{col 44}{space 1}   -7.04{col 53}{space 3}0.000{col 61}{space 4}-.1388122{col 74}{space 3}-.0782114
{txt}{space 19} {c |}
{space 16}edr {c |}
{space 17}1  {c |}{col 21}{res}{space 2} .0677268{col 33}{space 2} .0205869{col 44}{space 1}    3.29{col 53}{space 3}0.001{col 61}{space 4} .0272843{col 74}{space 3} .1081694
{txt}{space 17}2  {c |}{col 21}{res}{space 2}  .020045{col 33}{space 2} .0207418{col 44}{space 1}    0.97{col 53}{space 3}0.334{col 61}{space 4}-.0207018{col 74}{space 3} .0607919
{txt}{space 17}3  {c |}{col 21}{res}{space 2}-.0317218{col 33}{space 2} .0217134{col 44}{space 1}   -1.46{col 53}{space 3}0.145{col 61}{space 4}-.0743775{col 74}{space 3} .0109339
{txt}{space 19} {c |}
{space 12}1.rural {c |}{col 21}{res}{space 2} .0178454{col 33}{space 2}  .009145{col 44}{space 1}    1.95{col 53}{space 3}0.052{col 61}{space 4}-.0001199{col 74}{space 3} .0358107
{txt}{space 17}m1 {c |}{col 21}{res}{space 2}-.0153733{col 33}{space 2} .0044377{col 44}{space 1}   -3.46{col 53}{space 3}0.001{col 61}{space 4}-.0240912{col 74}{space 3}-.0066554
{txt}{space 15}ing4 {c |}{col 21}{res}{space 2}-.0016497{col 33}{space 2} .0023338{col 44}{space 1}   -0.71{col 53}{space 3}0.480{col 61}{space 4}-.0062344{col 74}{space 3}  .002935
{txt}{space 19} {c |}
{space 14}idio2 {c |}
{space 14}Same  {c |}{col 21}{res}{space 2} .0070194{col 33}{space 2} .0115219{col 44}{space 1}    0.61{col 53}{space 3}0.543{col 61}{space 4}-.0156151{col 74}{space 3} .0296539
{txt}{space 13}Worse  {c |}{col 21}{res}{space 2} .0156522{col 33}{space 2} .0130942{col 44}{space 1}    1.20{col 53}{space 3}0.232{col 61}{space 4}-.0100712{col 74}{space 3} .0413756
{txt}{space 19} {c |}
{space 14}soct2 {c |}
{space 14}Same  {c |}{col 21}{res}{space 2}-.0004986{col 33}{space 2} .0141382{col 44}{space 1}   -0.04{col 53}{space 3}0.972{col 61}{space 4}-.0282729{col 74}{space 3} .0272757
{txt}{space 13}Worse  {c |}{col 21}{res}{space 2} -.002427{col 33}{space 2} .0146634{col 44}{space 1}   -0.17{col 53}{space 3}0.869{col 61}{space 4} -.031233{col 74}{space 3} .0263789
{txt}{space 19} {c |}
{space 12}exc7new {c |}{col 21}{res}{space 2}-.0035989{col 33}{space 2} .0039566{col 44}{space 1}   -0.91{col 53}{space 3}0.363{col 61}{space 4}-.0113716{col 74}{space 3} .0041738
{txt}{space 15}psa5 {c |}{col 21}{res}{space 2}  .000612{col 33}{space 2} .0001939{col 44}{space 1}    3.16{col 53}{space 3}0.002{col 61}{space 4}  .000231{col 74}{space 3} .0009929
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2} .0727547{col 33}{space 2} .0157011{col 44}{space 1}    4.63{col 53}{space 3}0.000{col 61}{space 4} .0419101{col 74}{space 3} .1035992
{txt}{space 9}Nicaragua  {c |}{col 21}{res}{space 2} .0584862{col 33}{space 2} .0158048{col 44}{space 1}    3.70{col 53}{space 3}0.000{col 61}{space 4}  .027438{col 74}{space 3} .0895344
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2} .0255112{col 33}{space 2} .0137271{col 44}{space 1}    1.86{col 53}{space 3}0.064{col 61}{space 4}-.0014555{col 74}{space 3} .0524779
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2}  .068537{col 33}{space 2} .0129881{col 44}{space 1}    5.28{col 53}{space 3}0.000{col 61}{space 4} .0430221{col 74}{space 3} .0940518
{txt}Dominican Republic  {c |}{col 21}{res}{space 2} .1634879{col 33}{space 2} .0164099{col 44}{space 1}    9.96{col 53}{space 3}0.000{col 61}{space 4} .1312509{col 74}{space 3} .1957249
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2} .0985821{col 33}{space 2} .0175716{col 44}{space 1}    5.61{col 53}{space 3}0.000{col 61}{space 4} .0640629{col 74}{space 3} .1331012
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} .3106042{col 33}{space 2} .0369256{col 44}{space 1}    8.41{col 53}{space 3}0.000{col 61}{space 4} .2380645{col 74}{space 3} .3831439
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         margins woman // male slightly (.02) more likely approvers
{res}
{txt}Predictive margins

{col 1}{lalign 16:Number of strata}{col 9} = {res}{ralign 3:35}{txt}{col 52}{lalign 15:Number of obs}{col 67} = {res}{ralign 9:5,155}
{txt}{col 1}{lalign 16:Number of PSUs}{col 9} = {res}{ralign 3:561}{txt}{col 52}{lalign 15:Population size}{col 67} = {res}{ralign 9:5,026.595}
{txt}{col 1}Model VCE: {res:Linearized}{col 52}{lalign 15:Design df}{col 67} = {res}{ralign 9:526}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}woman {c |}
{space 10}0  {c |}{col 14}{res}{space 2} .3400228{col 26}{space 2} .0056702{col 37}{space 1}   59.97{col 46}{space 3}0.000{col 54}{space 4} .3288838{col 67}{space 3} .3511619
{txt}{space 10}1  {c |}{col 14}{res}{space 2} .3233402{col 26}{space 2} .0056692{col 37}{space 1}   57.03{col 46}{space 3}0.000{col 54}{space 4} .3122031{col 67}{space 3} .3344773
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         margins rural // rural 
{res}
{txt}Predictive margins

{col 1}{lalign 16:Number of strata}{col 9} = {res}{ralign 3:35}{txt}{col 52}{lalign 15:Number of obs}{col 67} = {res}{ralign 9:5,155}
{txt}{col 1}{lalign 16:Number of PSUs}{col 9} = {res}{ralign 3:561}{txt}{col 52}{lalign 15:Population size}{col 67} = {res}{ralign 9:5,026.595}
{txt}{col 1}Model VCE: {res:Linearized}{col 52}{lalign 15:Design df}{col 67} = {res}{ralign 9:526}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}rural {c |}
{space 10}0  {c |}{col 14}{res}{space 2} .3258152{col 26}{space 2} .0050938{col 37}{space 1}   63.96{col 46}{space 3}0.000{col 54}{space 4} .3158086{col 67}{space 3} .3358219
{txt}{space 10}1  {c |}{col 14}{res}{space 2} .3436607{col 26}{space 2} .0071546{col 37}{space 1}   48.03{col 46}{space 3}0.000{col 54}{space 4} .3296055{col 67}{space 3} .3577158
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         margins quintall // 2, then 3
{res}
{txt}Predictive margins

{col 1}{lalign 16:Number of strata}{col 9} = {res}{ralign 3:35}{txt}{col 52}{lalign 15:Number of obs}{col 67} = {res}{ralign 9:5,155}
{txt}{col 1}{lalign 16:Number of PSUs}{col 9} = {res}{ralign 3:561}{txt}{col 52}{lalign 15:Population size}{col 67} = {res}{ralign 9:5,026.595}
{txt}{col 1}Model VCE: {res:Linearized}{col 52}{lalign 15:Design df}{col 67} = {res}{ralign 9:526}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}quintall {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .3382964{col 26}{space 2} .0091914{col 37}{space 1}   36.81{col 46}{space 3}0.000{col 54}{space 4} .3202401{col 67}{space 3} .3563528
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .3422699{col 26}{space 2} .0083568{col 37}{space 1}   40.96{col 46}{space 3}0.000{col 54}{space 4} .3258531{col 67}{space 3} .3586868
{txt}{space 10}3  {c |}{col 14}{res}{space 2}  .341442{col 26}{space 2} .0086216{col 37}{space 1}   39.60{col 46}{space 3}0.000{col 54}{space 4}  .324505{col 67}{space 3}  .358379
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .3324663{col 26}{space 2} .0080374{col 37}{space 1}   41.36{col 46}{space 3}0.000{col 54}{space 4} .3166769{col 67}{space 3} .3482557
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .3075405{col 26}{space 2} .0088699{col 37}{space 1}   34.67{col 46}{space 3}0.000{col 54}{space 4} .2901157{col 67}{space 3} .3249653
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         margins agecohort // 1, then 0
{res}
{txt}Predictive margins

{col 1}{lalign 16:Number of strata}{col 9} = {res}{ralign 3:35}{txt}{col 52}{lalign 15:Number of obs}{col 67} = {res}{ralign 9:5,155}
{txt}{col 1}{lalign 16:Number of PSUs}{col 9} = {res}{ralign 3:561}{txt}{col 52}{lalign 15:Population size}{col 67} = {res}{ralign 9:5,026.595}
{txt}{col 1}Model VCE: {res:Linearized}{col 52}{lalign 15:Design df}{col 67} = {res}{ralign 9:526}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}agecohort {c |}
{space 10}0  {c |}{col 14}{res}{space 2} .3627932{col 26}{space 2} .0074012{col 37}{space 1}   49.02{col 46}{space 3}0.000{col 54}{space 4} .3482538{col 67}{space 3} .3773327
{txt}{space 10}1  {c |}{col 14}{res}{space 2} .3710137{col 26}{space 2} .0084761{col 37}{space 1}   43.77{col 46}{space 3}0.000{col 54}{space 4} .3543625{col 67}{space 3}  .387665
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .3256624{col 26}{space 2} .0093613{col 37}{space 1}   34.79{col 46}{space 3}0.000{col 54}{space 4} .3072722{col 67}{space 3} .3440526
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .2854304{col 26}{space 2} .0093258{col 37}{space 1}   30.61{col 46}{space 3}0.000{col 54}{space 4}   .26711{col 67}{space 3} .3037509
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .2813545{col 26}{space 2} .0121083{col 37}{space 1}   23.24{col 46}{space 3}0.000{col 54}{space 4}  .257568{col 67}{space 3}  .305141
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .2542814{col 26}{space 2} .0129908{col 37}{space 1}   19.57{col 46}{space 3}0.000{col 54}{space 4} .2287613{col 67}{space 3} .2798016
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         margins edr
{res}
{txt}Predictive margins

{col 1}{lalign 16:Number of strata}{col 9} = {res}{ralign 3:35}{txt}{col 52}{lalign 15:Number of obs}{col 67} = {res}{ralign 9:5,155}
{txt}{col 1}{lalign 16:Number of PSUs}{col 9} = {res}{ralign 3:561}{txt}{col 52}{lalign 15:Population size}{col 67} = {res}{ralign 9:5,026.595}
{txt}{col 1}Model VCE: {res:Linearized}{col 52}{lalign 15:Design df}{col 67} = {res}{ralign 9:526}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}edr {c |}
{space 10}0  {c |}{col 14}{res}{space 2} .3133277{col 26}{space 2} .0199273{col 37}{space 1}   15.72{col 46}{space 3}0.000{col 54}{space 4} .2741809{col 67}{space 3} .3524745
{txt}{space 10}1  {c |}{col 14}{res}{space 2} .3810545{col 26}{space 2} .0089219{col 37}{space 1}   42.71{col 46}{space 3}0.000{col 54}{space 4} .3635276{col 67}{space 3} .3985815
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .3333727{col 26}{space 2} .0059237{col 37}{space 1}   56.28{col 46}{space 3}0.000{col 54}{space 4} .3217357{col 67}{space 3} .3450098
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .2816059{col 26}{space 2} .0084406{col 37}{space 1}   33.36{col 46}{space 3}0.000{col 54}{space 4} .2650245{col 67}{space 3} .2981873
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         margins, at(m1=(1(1)5)) // 1 (very good)
{res}
{txt}Predictive margins

{col 1}{lalign 16:Number of strata}{col 9} = {res}{ralign 3:35}{txt}{col 52}{lalign 15:Number of obs}{col 67} = {res}{ralign 9:5,155}
{txt}{col 1}{lalign 16:Number of PSUs}{col 9} = {res}{ralign 3:561}{txt}{col 52}{lalign 15:Population size}{col 67} = {res}{ralign 9:5,026.595}
{txt}{col 1}Model VCE: {res:Linearized}{col 52}{lalign 15:Design df}{col 67} = {res}{ralign 9:526}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 2:m1} = {res:{ralign 1:1}}
{lalign 7:2._at: }{space 0}{lalign 2:m1} = {res:{ralign 1:2}}
{lalign 7:3._at: }{space 0}{lalign 2:m1} = {res:{ralign 1:3}}
{lalign 7:4._at: }{space 0}{lalign 2:m1} = {res:{ralign 1:4}}
{lalign 7:5._at: }{space 0}{lalign 2:m1} = {res:{ralign 1:5}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .3584975{col 26}{space 2} .0086313{col 37}{space 1}   41.53{col 46}{space 3}0.000{col 54}{space 4} .3415413{col 67}{space 3} .3754536
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .3431242{col 26}{space 2} .0051155{col 37}{space 1}   67.08{col 46}{space 3}0.000{col 54}{space 4} .3330748{col 67}{space 3} .3531735
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .3277509{col 26}{space 2} .0041502{col 37}{space 1}   78.97{col 46}{space 3}0.000{col 54}{space 4} .3195979{col 67}{space 3} .3359039
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .3123776{col 26}{space 2} .0069041{col 37}{space 1}   45.25{col 46}{space 3}0.000{col 54}{space 4} .2988146{col 67}{space 3} .3259407
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .2970043{col 26}{space 2} .0108396{col 37}{space 1}   27.40{col 46}{space 3}0.000{col 54}{space 4} .2757101{col 67}{space 3} .3182986
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         margins, at(psa5=(0(25)100)) // 100
{res}
{txt}Predictive margins

{col 1}{lalign 16:Number of strata}{col 9} = {res}{ralign 3:35}{txt}{col 52}{lalign 15:Number of obs}{col 67} = {res}{ralign 9:5,155}
{txt}{col 1}{lalign 16:Number of PSUs}{col 9} = {res}{ralign 3:561}{txt}{col 52}{lalign 15:Population size}{col 67} = {res}{ralign 9:5,026.595}
{txt}{col 1}Model VCE: {res:Linearized}{col 52}{lalign 15:Design df}{col 67} = {res}{ralign 9:526}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 4:psa5} = {res:{ralign 3:0}}
{lalign 7:2._at: }{space 0}{lalign 4:psa5} = {res:{ralign 3:25}}
{lalign 7:3._at: }{space 0}{lalign 4:psa5} = {res:{ralign 3:50}}
{lalign 7:4._at: }{space 0}{lalign 4:psa5} = {res:{ralign 3:75}}
{lalign 7:5._at: }{space 0}{lalign 4:psa5} = {res:{ralign 3:100}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .3024673{col 26}{space 2} .0101441{col 37}{space 1}   29.82{col 46}{space 3}0.000{col 54}{space 4} .2825393{col 67}{space 3} .3223953
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .3177661{col 26}{space 2} .0059909{col 37}{space 1}   53.04{col 46}{space 3}0.000{col 54}{space 4} .3059972{col 67}{space 3} .3295351
{txt}{space 10}3  {c |}{col 14}{res}{space 2}  .333065{col 26}{space 2} .0039859{col 37}{space 1}   83.56{col 46}{space 3}0.000{col 54}{space 4} .3252346{col 67}{space 3} .3408953
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .3483638{col 26}{space 2} .0065494{col 37}{space 1}   53.19{col 46}{space 3}0.000{col 54}{space 4} .3354975{col 67}{space 3} .3612301
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .3636626{col 26}{space 2} .0108126{col 37}{space 1}   33.63{col 46}{space 3}0.000{col 54}{space 4} .3424214{col 67}{space 3} .3849039
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         *non-approver profile: margins, at(rural=0 woman=1 agecohort=5 edr=3 quintall=5 m1=5 psa5=0)
.         *approver profile: margins, at(rural=1 woman=0 agecohort=1 edr=1 quintall=2 m1=1 psa5=100)
. 
.         svy: reg trustel direct woman quintall agecohort edr rural m1 ing4 psa5 i.idio2 i.soct2 i.pais if wave==2014 
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:118}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:31,029}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:1,726}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:29,269.078}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,608}
{txt}{col 51}{lalign 15:F({res:34}, {res:1575})}{col 66} = {res}{ralign 10:412.34}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.3405}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}            trustel{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}direct {c |}{col 21}{res}{space 2}-.0568418{col 33}{space 2} .0358525{col 44}{space 1}   -1.59{col 53}{space 3}0.113{col 61}{space 4}-.1271644{col 74}{space 3} .0134808
{txt}{space 14}woman {c |}{col 21}{res}{space 2}-.0698256{col 33}{space 2} .0183159{col 44}{space 1}   -3.81{col 53}{space 3}0.000{col 61}{space 4}-.1057511{col 74}{space 3}-.0339001
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}-.0023734{col 33}{space 2} .0078488{col 44}{space 1}   -0.30{col 53}{space 3}0.762{col 61}{space 4}-.0177685{col 74}{space 3} .0130216
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2}  .045389{col 33}{space 2} .0064592{col 44}{space 1}    7.03{col 53}{space 3}0.000{col 61}{space 4} .0327197{col 74}{space 3} .0580582
{txt}{space 16}edr {c |}{col 21}{res}{space 2} .0287374{col 33}{space 2}  .015299{col 44}{space 1}    1.88{col 53}{space 3}0.061{col 61}{space 4}-.0012707{col 74}{space 3} .0587455
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .0835634{col 33}{space 2} .0261769{col 44}{space 1}    3.19{col 53}{space 3}0.001{col 61}{space 4} .0322189{col 74}{space 3} .1349079
{txt}{space 17}m1 {c |}{col 21}{res}{space 2}-.3652865{col 33}{space 2} .0125638{col 44}{space 1}  -29.07{col 53}{space 3}0.000{col 61}{space 4}-.3899297{col 74}{space 3}-.3406433
{txt}{space 15}ing4 {c |}{col 21}{res}{space 2}   .09075{col 33}{space 2} .0064654{col 44}{space 1}   14.04{col 53}{space 3}0.000{col 61}{space 4} .0780685{col 74}{space 3} .1034316
{txt}{space 15}psa5 {c |}{col 21}{res}{space 2} .0307199{col 33}{space 2}  .000507{col 44}{space 1}   60.59{col 53}{space 3}0.000{col 61}{space 4} .0297254{col 74}{space 3} .0317144
{txt}{space 19} {c |}
{space 14}idio2 {c |}
{space 14}Same  {c |}{col 21}{res}{space 2}-.0143021{col 33}{space 2} .0280141{col 44}{space 1}   -0.51{col 53}{space 3}0.610{col 61}{space 4}-.0692501{col 74}{space 3} .0406459
{txt}{space 13}Worse  {c |}{col 21}{res}{space 2}-.0130756{col 33}{space 2} .0317589{col 44}{space 1}   -0.41{col 53}{space 3}0.681{col 61}{space 4}-.0753688{col 74}{space 3} .0492175
{txt}{space 19} {c |}
{space 14}soct2 {c |}
{space 14}Same  {c |}{col 21}{res}{space 2}-.1094102{col 33}{space 2}  .030684{col 44}{space 1}   -3.57{col 53}{space 3}0.000{col 61}{space 4}-.1695951{col 74}{space 3}-.0492253
{txt}{space 13}Worse  {c |}{col 21}{res}{space 2}-.2283386{col 33}{space 2} .0340712{col 44}{space 1}   -6.70{col 53}{space 3}0.000{col 61}{space 4}-.2951673{col 74}{space 3}  -.16151
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2} .0145715{col 33}{space 2} .0790104{col 44}{space 1}    0.18{col 53}{space 3}0.854{col 61}{space 4}-.1404027{col 74}{space 3} .1695456
{txt}{space 7}El Salvador  {c |}{col 21}{res}{space 2} .4580328{col 33}{space 2} .0705161{col 44}{space 1}    6.50{col 53}{space 3}0.000{col 61}{space 4} .3197196{col 74}{space 3}  .596346
{txt}{space 10}Honduras  {c |}{col 21}{res}{space 2}  -.18274{col 33}{space 2} .0722195{col 44}{space 1}   -2.53{col 53}{space 3}0.011{col 61}{space 4}-.3243942{col 74}{space 3}-.0410857
{txt}{space 9}Nicaragua  {c |}{col 21}{res}{space 2} .0130983{col 33}{space 2} .0702144{col 44}{space 1}    0.19{col 53}{space 3}0.852{col 61}{space 4}-.1246231{col 74}{space 3} .1508197
{txt}{space 8}Costa Rica  {c |}{col 21}{res}{space 2} .8621271{col 33}{space 2} .0758744{col 44}{space 1}   11.36{col 53}{space 3}0.000{col 61}{space 4} .7133039{col 74}{space 3}  1.01095
{txt}{space 12}Panama  {c |}{col 21}{res}{space 2} .4034901{col 33}{space 2} .0751418{col 44}{space 1}    5.37{col 53}{space 3}0.000{col 61}{space 4} .2561038{col 74}{space 3} .5508764
{txt}{space 10}Colombia  {c |}{col 21}{res}{space 2}  -.38225{col 33}{space 2} .0704269{col 44}{space 1}   -5.43{col 53}{space 3}0.000{col 61}{space 4}-.5203882{col 74}{space 3}-.2441118
{txt}{space 11}Ecuador  {c |}{col 21}{res}{space 2} .4416938{col 33}{space 2} .0760801{col 44}{space 1}    5.81{col 53}{space 3}0.000{col 61}{space 4} .2924673{col 74}{space 3} .5909204
{txt}{space 11}Bolivia  {c |}{col 21}{res}{space 2} .2452513{col 33}{space 2} .0642912{col 44}{space 1}    3.81{col 53}{space 3}0.000{col 61}{space 4} .1191478{col 74}{space 3} .3713547
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2} .5067641{col 33}{space 2} .0766983{col 44}{space 1}    6.61{col 53}{space 3}0.000{col 61}{space 4} .3563249{col 74}{space 3} .6572033
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2} .5201214{col 33}{space 2} .0732259{col 44}{space 1}    7.10{col 53}{space 3}0.000{col 61}{space 4} .3764932{col 74}{space 3} .6637496
{txt}{space 13}Chile  {c |}{col 21}{res}{space 2} 1.053668{col 33}{space 2} .0808451{col 44}{space 1}   13.03{col 53}{space 3}0.000{col 61}{space 4} .8950955{col 74}{space 3} 1.212241
{txt}{space 11}Uruguay  {c |}{col 21}{res}{space 2} 1.507702{col 33}{space 2} .0727551{col 44}{space 1}   20.72{col 53}{space 3}0.000{col 61}{space 4} 1.364997{col 74}{space 3} 1.650407
{txt}{space 12}Brazil  {c |}{col 21}{res}{space 2} -.066399{col 33}{space 2} .0749998{col 44}{space 1}   -0.89{col 53}{space 3}0.376{col 61}{space 4}-.2135066{col 74}{space 3} .0807087
{txt}{space 9}Venezuela  {c |}{col 21}{res}{space 2} .6606388{col 33}{space 2} .0803214{col 44}{space 1}    8.22{col 53}{space 3}0.000{col 61}{space 4} .5030932{col 74}{space 3} .8181844
{txt}{space 9}Argentina  {c |}{col 21}{res}{space 2} .7926087{col 33}{space 2} .0760784{col 44}{space 1}   10.42{col 53}{space 3}0.000{col 61}{space 4} .6433855{col 74}{space 3} .9418319
{txt}Dominican Republic  {c |}{col 21}{res}{space 2} -.275362{col 33}{space 2} .0748443{col 44}{space 1}   -3.68{col 53}{space 3}0.000{col 61}{space 4}-.4221646{col 74}{space 3}-.1285594
{txt}{space 13}Haiti  {c |}{col 21}{res}{space 2}-.7263539{col 33}{space 2} .0757352{col 44}{space 1}   -9.59{col 53}{space 3}0.000{col 61}{space 4} -.874904{col 74}{space 3}-.5778038
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2} .1065227{col 33}{space 2} .0660964{col 44}{space 1}    1.61{col 53}{space 3}0.107{col 61}{space 4}-.0231214{col 74}{space 3} .2361669
{txt}{space 12}Guyana  {c |}{col 21}{res}{space 2} .0884311{col 33}{space 2} .0965535{col 44}{space 1}    0.92{col 53}{space 3}0.360{col 61}{space 4}-.1009528{col 74}{space 3}  .277815
{txt}{space 12}Belize  {c |}{col 21}{res}{space 2}  .319438{col 33}{space 2} .0670424{col 44}{space 1}    4.76{col 53}{space 3}0.000{col 61}{space 4} .1879384{col 74}{space 3} .4509376
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} 2.582915{col 33}{space 2} .0913486{col 44}{space 1}   28.28{col 53}{space 3}0.000{col 61}{space 4} 2.403741{col 74}{space 3}  2.76209
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.                 margins, at(direct=0 rural=1 woman=0 agecohort=1 edr=1 quintall=2 m1=1 psa5=100) atmeans post // approver, no exposure
{res}
{txt}Adjusted predictions

{col 1}{lalign 16:Number of strata}{col 9} = {res}{ralign 5:118}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:31,029}
{txt}{col 1}{lalign 16:Number of PSUs}{col 9} = {res}{ralign 5:1,726}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:29,269.078}
{txt}{col 1}Model VCE: {res:Linearized}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,608}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 4:At: }{space 0}{lalign 9:direct} = {res:{ralign 8:0}}
{lalign 4:}{space 0}{lalign 9:woman} = {res:{ralign 8:0}}
{lalign 4:}{space 0}{lalign 9:quintall} = {res:{ralign 8:2}}
{lalign 4:}{space 0}{lalign 9:agecohort} = {res:{ralign 8:1}}
{lalign 4:}{space 0}{lalign 9:edr} = {res:{ralign 8:1}}
{lalign 4:}{space 0}{lalign 9:rural} = {res:{ralign 8:1}}
{lalign 4:}{space 0}{lalign 9:m1} = {res:{ralign 8:1}}
{lalign 4:}{space 0}{lalign 9:ing4} = {res:{ralign 8:5.176212}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:psa5} = {res:{ralign 8:100}}
{lalign 4:}{space 0}{lalign 9:1.idio2} = {res:{ralign 8:.2003861}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:2.idio2} = {res:{ralign 8:.448288}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:3.idio2} = {res:{ralign 8:.3513259}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:1.soct2} = {res:{ralign 8:.158015}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:2.soct2} = {res:{ralign 8:.3663513}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:3.soct2} = {res:{ralign 8:.4756337}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:1.pais} = {res:{ralign 8:.0450387}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:2.pais} = {res:{ralign 8:.0444087}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:3.pais} = {res:{ralign 8:.0496217}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:4.pais} = {res:{ralign 8:.0474731}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:5.pais} = {res:{ralign 8:.0479337}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:6.pais} = {res:{ralign 8:.0471141}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:7.pais} = {res:{ralign 8:.0474424}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:8.pais} = {res:{ralign 8:.0463156}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:9.pais} = {res:{ralign 8:.0453287}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:10.pais} = {res:{ralign 8:.0460812}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:11.pais} = {res:{ralign 8:.0435272}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:12.pais} = {res:{ralign 8:.0415649}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:13.pais} = {res:{ralign 8:.0396103}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:14.pais} = {res:{ralign 8:.0477575}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:15.pais} = {res:{ralign 8:.0483742}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:16.pais} = {res:{ralign 8:.0468071}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:17.pais} = {res:{ralign 8:.0439952}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:21.pais} = {res:{ralign 8:.0482479}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:22.pais} = {res:{ralign 8:.040199}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:23.pais} = {res:{ralign 8:.0434062}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:24.pais} = {res:{ralign 8:.0431845}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:26.pais} = {res:{ralign 8:.0465684}} {txt:(mean)}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} 6.042154{col 26}{space 2} .0362728{col 37}{space 1}  166.58{col 46}{space 3}0.000{col 54}{space 4} 5.971007{col 67}{space 3} 6.113301
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.                 eststo m1
{txt}
{com}.         svy: reg trustel direct woman quintall agecohort edr rural m1 ing4 psa5 i.idio2 i.soct2 i.pais if wave==2014
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:118}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:31,029}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:1,726}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:29,269.078}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,608}
{txt}{col 51}{lalign 15:F({res:34}, {res:1575})}{col 66} = {res}{ralign 10:412.34}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.3405}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}            trustel{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}direct {c |}{col 21}{res}{space 2}-.0568418{col 33}{space 2} .0358525{col 44}{space 1}   -1.59{col 53}{space 3}0.113{col 61}{space 4}-.1271644{col 74}{space 3} .0134808
{txt}{space 14}woman {c |}{col 21}{res}{space 2}-.0698256{col 33}{space 2} .0183159{col 44}{space 1}   -3.81{col 53}{space 3}0.000{col 61}{space 4}-.1057511{col 74}{space 3}-.0339001
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}-.0023734{col 33}{space 2} .0078488{col 44}{space 1}   -0.30{col 53}{space 3}0.762{col 61}{space 4}-.0177685{col 74}{space 3} .0130216
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2}  .045389{col 33}{space 2} .0064592{col 44}{space 1}    7.03{col 53}{space 3}0.000{col 61}{space 4} .0327197{col 74}{space 3} .0580582
{txt}{space 16}edr {c |}{col 21}{res}{space 2} .0287374{col 33}{space 2}  .015299{col 44}{space 1}    1.88{col 53}{space 3}0.061{col 61}{space 4}-.0012707{col 74}{space 3} .0587455
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .0835634{col 33}{space 2} .0261769{col 44}{space 1}    3.19{col 53}{space 3}0.001{col 61}{space 4} .0322189{col 74}{space 3} .1349079
{txt}{space 17}m1 {c |}{col 21}{res}{space 2}-.3652865{col 33}{space 2} .0125638{col 44}{space 1}  -29.07{col 53}{space 3}0.000{col 61}{space 4}-.3899297{col 74}{space 3}-.3406433
{txt}{space 15}ing4 {c |}{col 21}{res}{space 2}   .09075{col 33}{space 2} .0064654{col 44}{space 1}   14.04{col 53}{space 3}0.000{col 61}{space 4} .0780685{col 74}{space 3} .1034316
{txt}{space 15}psa5 {c |}{col 21}{res}{space 2} .0307199{col 33}{space 2}  .000507{col 44}{space 1}   60.59{col 53}{space 3}0.000{col 61}{space 4} .0297254{col 74}{space 3} .0317144
{txt}{space 19} {c |}
{space 14}idio2 {c |}
{space 14}Same  {c |}{col 21}{res}{space 2}-.0143021{col 33}{space 2} .0280141{col 44}{space 1}   -0.51{col 53}{space 3}0.610{col 61}{space 4}-.0692501{col 74}{space 3} .0406459
{txt}{space 13}Worse  {c |}{col 21}{res}{space 2}-.0130756{col 33}{space 2} .0317589{col 44}{space 1}   -0.41{col 53}{space 3}0.681{col 61}{space 4}-.0753688{col 74}{space 3} .0492175
{txt}{space 19} {c |}
{space 14}soct2 {c |}
{space 14}Same  {c |}{col 21}{res}{space 2}-.1094102{col 33}{space 2}  .030684{col 44}{space 1}   -3.57{col 53}{space 3}0.000{col 61}{space 4}-.1695951{col 74}{space 3}-.0492253
{txt}{space 13}Worse  {c |}{col 21}{res}{space 2}-.2283386{col 33}{space 2} .0340712{col 44}{space 1}   -6.70{col 53}{space 3}0.000{col 61}{space 4}-.2951673{col 74}{space 3}  -.16151
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2} .0145715{col 33}{space 2} .0790104{col 44}{space 1}    0.18{col 53}{space 3}0.854{col 61}{space 4}-.1404027{col 74}{space 3} .1695456
{txt}{space 7}El Salvador  {c |}{col 21}{res}{space 2} .4580328{col 33}{space 2} .0705161{col 44}{space 1}    6.50{col 53}{space 3}0.000{col 61}{space 4} .3197196{col 74}{space 3}  .596346
{txt}{space 10}Honduras  {c |}{col 21}{res}{space 2}  -.18274{col 33}{space 2} .0722195{col 44}{space 1}   -2.53{col 53}{space 3}0.011{col 61}{space 4}-.3243942{col 74}{space 3}-.0410857
{txt}{space 9}Nicaragua  {c |}{col 21}{res}{space 2} .0130983{col 33}{space 2} .0702144{col 44}{space 1}    0.19{col 53}{space 3}0.852{col 61}{space 4}-.1246231{col 74}{space 3} .1508197
{txt}{space 8}Costa Rica  {c |}{col 21}{res}{space 2} .8621271{col 33}{space 2} .0758744{col 44}{space 1}   11.36{col 53}{space 3}0.000{col 61}{space 4} .7133039{col 74}{space 3}  1.01095
{txt}{space 12}Panama  {c |}{col 21}{res}{space 2} .4034901{col 33}{space 2} .0751418{col 44}{space 1}    5.37{col 53}{space 3}0.000{col 61}{space 4} .2561038{col 74}{space 3} .5508764
{txt}{space 10}Colombia  {c |}{col 21}{res}{space 2}  -.38225{col 33}{space 2} .0704269{col 44}{space 1}   -5.43{col 53}{space 3}0.000{col 61}{space 4}-.5203882{col 74}{space 3}-.2441118
{txt}{space 11}Ecuador  {c |}{col 21}{res}{space 2} .4416938{col 33}{space 2} .0760801{col 44}{space 1}    5.81{col 53}{space 3}0.000{col 61}{space 4} .2924673{col 74}{space 3} .5909204
{txt}{space 11}Bolivia  {c |}{col 21}{res}{space 2} .2452513{col 33}{space 2} .0642912{col 44}{space 1}    3.81{col 53}{space 3}0.000{col 61}{space 4} .1191478{col 74}{space 3} .3713547
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2} .5067641{col 33}{space 2} .0766983{col 44}{space 1}    6.61{col 53}{space 3}0.000{col 61}{space 4} .3563249{col 74}{space 3} .6572033
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2} .5201214{col 33}{space 2} .0732259{col 44}{space 1}    7.10{col 53}{space 3}0.000{col 61}{space 4} .3764932{col 74}{space 3} .6637496
{txt}{space 13}Chile  {c |}{col 21}{res}{space 2} 1.053668{col 33}{space 2} .0808451{col 44}{space 1}   13.03{col 53}{space 3}0.000{col 61}{space 4} .8950955{col 74}{space 3} 1.212241
{txt}{space 11}Uruguay  {c |}{col 21}{res}{space 2} 1.507702{col 33}{space 2} .0727551{col 44}{space 1}   20.72{col 53}{space 3}0.000{col 61}{space 4} 1.364997{col 74}{space 3} 1.650407
{txt}{space 12}Brazil  {c |}{col 21}{res}{space 2} -.066399{col 33}{space 2} .0749998{col 44}{space 1}   -0.89{col 53}{space 3}0.376{col 61}{space 4}-.2135066{col 74}{space 3} .0807087
{txt}{space 9}Venezuela  {c |}{col 21}{res}{space 2} .6606388{col 33}{space 2} .0803214{col 44}{space 1}    8.22{col 53}{space 3}0.000{col 61}{space 4} .5030932{col 74}{space 3} .8181844
{txt}{space 9}Argentina  {c |}{col 21}{res}{space 2} .7926087{col 33}{space 2} .0760784{col 44}{space 1}   10.42{col 53}{space 3}0.000{col 61}{space 4} .6433855{col 74}{space 3} .9418319
{txt}Dominican Republic  {c |}{col 21}{res}{space 2} -.275362{col 33}{space 2} .0748443{col 44}{space 1}   -3.68{col 53}{space 3}0.000{col 61}{space 4}-.4221646{col 74}{space 3}-.1285594
{txt}{space 13}Haiti  {c |}{col 21}{res}{space 2}-.7263539{col 33}{space 2} .0757352{col 44}{space 1}   -9.59{col 53}{space 3}0.000{col 61}{space 4} -.874904{col 74}{space 3}-.5778038
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2} .1065227{col 33}{space 2} .0660964{col 44}{space 1}    1.61{col 53}{space 3}0.107{col 61}{space 4}-.0231214{col 74}{space 3} .2361669
{txt}{space 12}Guyana  {c |}{col 21}{res}{space 2} .0884311{col 33}{space 2} .0965535{col 44}{space 1}    0.92{col 53}{space 3}0.360{col 61}{space 4}-.1009528{col 74}{space 3}  .277815
{txt}{space 12}Belize  {c |}{col 21}{res}{space 2}  .319438{col 33}{space 2} .0670424{col 44}{space 1}    4.76{col 53}{space 3}0.000{col 61}{space 4} .1879384{col 74}{space 3} .4509376
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} 2.582915{col 33}{space 2} .0913486{col 44}{space 1}   28.28{col 53}{space 3}0.000{col 61}{space 4} 2.403741{col 74}{space 3}  2.76209
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.                 margins, at(direct=1 rural=1 woman=0 agecohort=1 edr=1 quintall=2 m1=1 psa5=100) atmeans post // approver, vb exposure
{res}
{txt}Adjusted predictions

{col 1}{lalign 16:Number of strata}{col 9} = {res}{ralign 5:118}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:31,029}
{txt}{col 1}{lalign 16:Number of PSUs}{col 9} = {res}{ralign 5:1,726}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:29,269.078}
{txt}{col 1}Model VCE: {res:Linearized}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,608}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 4:At: }{space 0}{lalign 9:direct} = {res:{ralign 8:1}}
{lalign 4:}{space 0}{lalign 9:woman} = {res:{ralign 8:0}}
{lalign 4:}{space 0}{lalign 9:quintall} = {res:{ralign 8:2}}
{lalign 4:}{space 0}{lalign 9:agecohort} = {res:{ralign 8:1}}
{lalign 4:}{space 0}{lalign 9:edr} = {res:{ralign 8:1}}
{lalign 4:}{space 0}{lalign 9:rural} = {res:{ralign 8:1}}
{lalign 4:}{space 0}{lalign 9:m1} = {res:{ralign 8:1}}
{lalign 4:}{space 0}{lalign 9:ing4} = {res:{ralign 8:5.176212}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:psa5} = {res:{ralign 8:100}}
{lalign 4:}{space 0}{lalign 9:1.idio2} = {res:{ralign 8:.2003861}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:2.idio2} = {res:{ralign 8:.448288}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:3.idio2} = {res:{ralign 8:.3513259}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:1.soct2} = {res:{ralign 8:.158015}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:2.soct2} = {res:{ralign 8:.3663513}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:3.soct2} = {res:{ralign 8:.4756337}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:1.pais} = {res:{ralign 8:.0450387}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:2.pais} = {res:{ralign 8:.0444087}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:3.pais} = {res:{ralign 8:.0496217}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:4.pais} = {res:{ralign 8:.0474731}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:5.pais} = {res:{ralign 8:.0479337}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:6.pais} = {res:{ralign 8:.0471141}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:7.pais} = {res:{ralign 8:.0474424}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:8.pais} = {res:{ralign 8:.0463156}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:9.pais} = {res:{ralign 8:.0453287}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:10.pais} = {res:{ralign 8:.0460812}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:11.pais} = {res:{ralign 8:.0435272}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:12.pais} = {res:{ralign 8:.0415649}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:13.pais} = {res:{ralign 8:.0396103}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:14.pais} = {res:{ralign 8:.0477575}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:15.pais} = {res:{ralign 8:.0483742}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:16.pais} = {res:{ralign 8:.0468071}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:17.pais} = {res:{ralign 8:.0439952}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:21.pais} = {res:{ralign 8:.0482479}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:22.pais} = {res:{ralign 8:.040199}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:23.pais} = {res:{ralign 8:.0434062}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:24.pais} = {res:{ralign 8:.0431845}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:26.pais} = {res:{ralign 8:.0465684}} {txt:(mean)}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} 5.985312{col 26}{space 2}  .050398{col 37}{space 1}  118.76{col 46}{space 3}0.000{col 54}{space 4}  5.88646{col 67}{space 3} 6.084165
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.                 eststo m2
{txt}
{com}.         svy: reg trustel direct woman quintall agecohort edr rural m1 ing4 psa5 i.idio2 i.soct2 i.pais if wave==2014
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:118}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:31,029}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:1,726}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:29,269.078}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,608}
{txt}{col 51}{lalign 15:F({res:34}, {res:1575})}{col 66} = {res}{ralign 10:412.34}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.3405}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}            trustel{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}direct {c |}{col 21}{res}{space 2}-.0568418{col 33}{space 2} .0358525{col 44}{space 1}   -1.59{col 53}{space 3}0.113{col 61}{space 4}-.1271644{col 74}{space 3} .0134808
{txt}{space 14}woman {c |}{col 21}{res}{space 2}-.0698256{col 33}{space 2} .0183159{col 44}{space 1}   -3.81{col 53}{space 3}0.000{col 61}{space 4}-.1057511{col 74}{space 3}-.0339001
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}-.0023734{col 33}{space 2} .0078488{col 44}{space 1}   -0.30{col 53}{space 3}0.762{col 61}{space 4}-.0177685{col 74}{space 3} .0130216
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2}  .045389{col 33}{space 2} .0064592{col 44}{space 1}    7.03{col 53}{space 3}0.000{col 61}{space 4} .0327197{col 74}{space 3} .0580582
{txt}{space 16}edr {c |}{col 21}{res}{space 2} .0287374{col 33}{space 2}  .015299{col 44}{space 1}    1.88{col 53}{space 3}0.061{col 61}{space 4}-.0012707{col 74}{space 3} .0587455
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .0835634{col 33}{space 2} .0261769{col 44}{space 1}    3.19{col 53}{space 3}0.001{col 61}{space 4} .0322189{col 74}{space 3} .1349079
{txt}{space 17}m1 {c |}{col 21}{res}{space 2}-.3652865{col 33}{space 2} .0125638{col 44}{space 1}  -29.07{col 53}{space 3}0.000{col 61}{space 4}-.3899297{col 74}{space 3}-.3406433
{txt}{space 15}ing4 {c |}{col 21}{res}{space 2}   .09075{col 33}{space 2} .0064654{col 44}{space 1}   14.04{col 53}{space 3}0.000{col 61}{space 4} .0780685{col 74}{space 3} .1034316
{txt}{space 15}psa5 {c |}{col 21}{res}{space 2} .0307199{col 33}{space 2}  .000507{col 44}{space 1}   60.59{col 53}{space 3}0.000{col 61}{space 4} .0297254{col 74}{space 3} .0317144
{txt}{space 19} {c |}
{space 14}idio2 {c |}
{space 14}Same  {c |}{col 21}{res}{space 2}-.0143021{col 33}{space 2} .0280141{col 44}{space 1}   -0.51{col 53}{space 3}0.610{col 61}{space 4}-.0692501{col 74}{space 3} .0406459
{txt}{space 13}Worse  {c |}{col 21}{res}{space 2}-.0130756{col 33}{space 2} .0317589{col 44}{space 1}   -0.41{col 53}{space 3}0.681{col 61}{space 4}-.0753688{col 74}{space 3} .0492175
{txt}{space 19} {c |}
{space 14}soct2 {c |}
{space 14}Same  {c |}{col 21}{res}{space 2}-.1094102{col 33}{space 2}  .030684{col 44}{space 1}   -3.57{col 53}{space 3}0.000{col 61}{space 4}-.1695951{col 74}{space 3}-.0492253
{txt}{space 13}Worse  {c |}{col 21}{res}{space 2}-.2283386{col 33}{space 2} .0340712{col 44}{space 1}   -6.70{col 53}{space 3}0.000{col 61}{space 4}-.2951673{col 74}{space 3}  -.16151
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2} .0145715{col 33}{space 2} .0790104{col 44}{space 1}    0.18{col 53}{space 3}0.854{col 61}{space 4}-.1404027{col 74}{space 3} .1695456
{txt}{space 7}El Salvador  {c |}{col 21}{res}{space 2} .4580328{col 33}{space 2} .0705161{col 44}{space 1}    6.50{col 53}{space 3}0.000{col 61}{space 4} .3197196{col 74}{space 3}  .596346
{txt}{space 10}Honduras  {c |}{col 21}{res}{space 2}  -.18274{col 33}{space 2} .0722195{col 44}{space 1}   -2.53{col 53}{space 3}0.011{col 61}{space 4}-.3243942{col 74}{space 3}-.0410857
{txt}{space 9}Nicaragua  {c |}{col 21}{res}{space 2} .0130983{col 33}{space 2} .0702144{col 44}{space 1}    0.19{col 53}{space 3}0.852{col 61}{space 4}-.1246231{col 74}{space 3} .1508197
{txt}{space 8}Costa Rica  {c |}{col 21}{res}{space 2} .8621271{col 33}{space 2} .0758744{col 44}{space 1}   11.36{col 53}{space 3}0.000{col 61}{space 4} .7133039{col 74}{space 3}  1.01095
{txt}{space 12}Panama  {c |}{col 21}{res}{space 2} .4034901{col 33}{space 2} .0751418{col 44}{space 1}    5.37{col 53}{space 3}0.000{col 61}{space 4} .2561038{col 74}{space 3} .5508764
{txt}{space 10}Colombia  {c |}{col 21}{res}{space 2}  -.38225{col 33}{space 2} .0704269{col 44}{space 1}   -5.43{col 53}{space 3}0.000{col 61}{space 4}-.5203882{col 74}{space 3}-.2441118
{txt}{space 11}Ecuador  {c |}{col 21}{res}{space 2} .4416938{col 33}{space 2} .0760801{col 44}{space 1}    5.81{col 53}{space 3}0.000{col 61}{space 4} .2924673{col 74}{space 3} .5909204
{txt}{space 11}Bolivia  {c |}{col 21}{res}{space 2} .2452513{col 33}{space 2} .0642912{col 44}{space 1}    3.81{col 53}{space 3}0.000{col 61}{space 4} .1191478{col 74}{space 3} .3713547
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2} .5067641{col 33}{space 2} .0766983{col 44}{space 1}    6.61{col 53}{space 3}0.000{col 61}{space 4} .3563249{col 74}{space 3} .6572033
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2} .5201214{col 33}{space 2} .0732259{col 44}{space 1}    7.10{col 53}{space 3}0.000{col 61}{space 4} .3764932{col 74}{space 3} .6637496
{txt}{space 13}Chile  {c |}{col 21}{res}{space 2} 1.053668{col 33}{space 2} .0808451{col 44}{space 1}   13.03{col 53}{space 3}0.000{col 61}{space 4} .8950955{col 74}{space 3} 1.212241
{txt}{space 11}Uruguay  {c |}{col 21}{res}{space 2} 1.507702{col 33}{space 2} .0727551{col 44}{space 1}   20.72{col 53}{space 3}0.000{col 61}{space 4} 1.364997{col 74}{space 3} 1.650407
{txt}{space 12}Brazil  {c |}{col 21}{res}{space 2} -.066399{col 33}{space 2} .0749998{col 44}{space 1}   -0.89{col 53}{space 3}0.376{col 61}{space 4}-.2135066{col 74}{space 3} .0807087
{txt}{space 9}Venezuela  {c |}{col 21}{res}{space 2} .6606388{col 33}{space 2} .0803214{col 44}{space 1}    8.22{col 53}{space 3}0.000{col 61}{space 4} .5030932{col 74}{space 3} .8181844
{txt}{space 9}Argentina  {c |}{col 21}{res}{space 2} .7926087{col 33}{space 2} .0760784{col 44}{space 1}   10.42{col 53}{space 3}0.000{col 61}{space 4} .6433855{col 74}{space 3} .9418319
{txt}Dominican Republic  {c |}{col 21}{res}{space 2} -.275362{col 33}{space 2} .0748443{col 44}{space 1}   -3.68{col 53}{space 3}0.000{col 61}{space 4}-.4221646{col 74}{space 3}-.1285594
{txt}{space 13}Haiti  {c |}{col 21}{res}{space 2}-.7263539{col 33}{space 2} .0757352{col 44}{space 1}   -9.59{col 53}{space 3}0.000{col 61}{space 4} -.874904{col 74}{space 3}-.5778038
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2} .1065227{col 33}{space 2} .0660964{col 44}{space 1}    1.61{col 53}{space 3}0.107{col 61}{space 4}-.0231214{col 74}{space 3} .2361669
{txt}{space 12}Guyana  {c |}{col 21}{res}{space 2} .0884311{col 33}{space 2} .0965535{col 44}{space 1}    0.92{col 53}{space 3}0.360{col 61}{space 4}-.1009528{col 74}{space 3}  .277815
{txt}{space 12}Belize  {c |}{col 21}{res}{space 2}  .319438{col 33}{space 2} .0670424{col 44}{space 1}    4.76{col 53}{space 3}0.000{col 61}{space 4} .1879384{col 74}{space 3} .4509376
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} 2.582915{col 33}{space 2} .0913486{col 44}{space 1}   28.28{col 53}{space 3}0.000{col 61}{space 4} 2.403741{col 74}{space 3}  2.76209
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.                 margins, at(direct=0 rural=0 woman=1 agecohort=5 edr=3 quintall=5 m1=5 psa5=0) atmeans post // non-approver, no exposure
{res}
{txt}Adjusted predictions

{col 1}{lalign 16:Number of strata}{col 9} = {res}{ralign 5:118}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:31,029}
{txt}{col 1}{lalign 16:Number of PSUs}{col 9} = {res}{ralign 5:1,726}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:29,269.078}
{txt}{col 1}Model VCE: {res:Linearized}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,608}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 4:At: }{space 0}{lalign 9:direct} = {res:{ralign 8:0}}
{lalign 4:}{space 0}{lalign 9:woman} = {res:{ralign 8:1}}
{lalign 4:}{space 0}{lalign 9:quintall} = {res:{ralign 8:5}}
{lalign 4:}{space 0}{lalign 9:agecohort} = {res:{ralign 8:5}}
{lalign 4:}{space 0}{lalign 9:edr} = {res:{ralign 8:3}}
{lalign 4:}{space 0}{lalign 9:rural} = {res:{ralign 8:0}}
{lalign 4:}{space 0}{lalign 9:m1} = {res:{ralign 8:5}}
{lalign 4:}{space 0}{lalign 9:ing4} = {res:{ralign 8:5.176212}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:psa5} = {res:{ralign 8:0}}
{lalign 4:}{space 0}{lalign 9:1.idio2} = {res:{ralign 8:.2003861}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:2.idio2} = {res:{ralign 8:.448288}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:3.idio2} = {res:{ralign 8:.3513259}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:1.soct2} = {res:{ralign 8:.158015}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:2.soct2} = {res:{ralign 8:.3663513}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:3.soct2} = {res:{ralign 8:.4756337}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:1.pais} = {res:{ralign 8:.0450387}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:2.pais} = {res:{ralign 8:.0444087}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:3.pais} = {res:{ralign 8:.0496217}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:4.pais} = {res:{ralign 8:.0474731}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:5.pais} = {res:{ralign 8:.0479337}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:6.pais} = {res:{ralign 8:.0471141}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:7.pais} = {res:{ralign 8:.0474424}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:8.pais} = {res:{ralign 8:.0463156}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:9.pais} = {res:{ralign 8:.0453287}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:10.pais} = {res:{ralign 8:.0460812}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:11.pais} = {res:{ralign 8:.0435272}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:12.pais} = {res:{ralign 8:.0415649}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:13.pais} = {res:{ralign 8:.0396103}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:14.pais} = {res:{ralign 8:.0477575}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:15.pais} = {res:{ralign 8:.0483742}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:16.pais} = {res:{ralign 8:.0468071}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:17.pais} = {res:{ralign 8:.0439952}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:21.pais} = {res:{ralign 8:.0482479}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:22.pais} = {res:{ralign 8:.040199}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:23.pais} = {res:{ralign 8:.0434062}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:24.pais} = {res:{ralign 8:.0431845}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:26.pais} = {res:{ralign 8:.0465684}} {txt:(mean)}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} 1.587535{col 26}{space 2} .0457626{col 37}{space 1}   34.69{col 46}{space 3}0.000{col 54}{space 4} 1.497775{col 67}{space 3} 1.677296
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.                 eststo m3
{txt}
{com}.         svy: reg trustel direct woman quintall agecohort edr rural m1 ing4 psa5 i.idio2 i.soct2 i.pais if wave==2014
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:118}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:31,029}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:1,726}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:29,269.078}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,608}
{txt}{col 51}{lalign 15:F({res:34}, {res:1575})}{col 66} = {res}{ralign 10:412.34}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0000}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.3405}

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}  Linearized
{col 1}            trustel{col 21}{c |} Coefficient{col 33}  std. err.{col 45}      t{col 53}   P>|t|{col 61}     [95% con{col 74}f. interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}direct {c |}{col 21}{res}{space 2}-.0568418{col 33}{space 2} .0358525{col 44}{space 1}   -1.59{col 53}{space 3}0.113{col 61}{space 4}-.1271644{col 74}{space 3} .0134808
{txt}{space 14}woman {c |}{col 21}{res}{space 2}-.0698256{col 33}{space 2} .0183159{col 44}{space 1}   -3.81{col 53}{space 3}0.000{col 61}{space 4}-.1057511{col 74}{space 3}-.0339001
{txt}{space 11}quintall {c |}{col 21}{res}{space 2}-.0023734{col 33}{space 2} .0078488{col 44}{space 1}   -0.30{col 53}{space 3}0.762{col 61}{space 4}-.0177685{col 74}{space 3} .0130216
{txt}{space 10}agecohort {c |}{col 21}{res}{space 2}  .045389{col 33}{space 2} .0064592{col 44}{space 1}    7.03{col 53}{space 3}0.000{col 61}{space 4} .0327197{col 74}{space 3} .0580582
{txt}{space 16}edr {c |}{col 21}{res}{space 2} .0287374{col 33}{space 2}  .015299{col 44}{space 1}    1.88{col 53}{space 3}0.061{col 61}{space 4}-.0012707{col 74}{space 3} .0587455
{txt}{space 14}rural {c |}{col 21}{res}{space 2} .0835634{col 33}{space 2} .0261769{col 44}{space 1}    3.19{col 53}{space 3}0.001{col 61}{space 4} .0322189{col 74}{space 3} .1349079
{txt}{space 17}m1 {c |}{col 21}{res}{space 2}-.3652865{col 33}{space 2} .0125638{col 44}{space 1}  -29.07{col 53}{space 3}0.000{col 61}{space 4}-.3899297{col 74}{space 3}-.3406433
{txt}{space 15}ing4 {c |}{col 21}{res}{space 2}   .09075{col 33}{space 2} .0064654{col 44}{space 1}   14.04{col 53}{space 3}0.000{col 61}{space 4} .0780685{col 74}{space 3} .1034316
{txt}{space 15}psa5 {c |}{col 21}{res}{space 2} .0307199{col 33}{space 2}  .000507{col 44}{space 1}   60.59{col 53}{space 3}0.000{col 61}{space 4} .0297254{col 74}{space 3} .0317144
{txt}{space 19} {c |}
{space 14}idio2 {c |}
{space 14}Same  {c |}{col 21}{res}{space 2}-.0143021{col 33}{space 2} .0280141{col 44}{space 1}   -0.51{col 53}{space 3}0.610{col 61}{space 4}-.0692501{col 74}{space 3} .0406459
{txt}{space 13}Worse  {c |}{col 21}{res}{space 2}-.0130756{col 33}{space 2} .0317589{col 44}{space 1}   -0.41{col 53}{space 3}0.681{col 61}{space 4}-.0753688{col 74}{space 3} .0492175
{txt}{space 19} {c |}
{space 14}soct2 {c |}
{space 14}Same  {c |}{col 21}{res}{space 2}-.1094102{col 33}{space 2}  .030684{col 44}{space 1}   -3.57{col 53}{space 3}0.000{col 61}{space 4}-.1695951{col 74}{space 3}-.0492253
{txt}{space 13}Worse  {c |}{col 21}{res}{space 2}-.2283386{col 33}{space 2} .0340712{col 44}{space 1}   -6.70{col 53}{space 3}0.000{col 61}{space 4}-.2951673{col 74}{space 3}  -.16151
{txt}{space 19} {c |}
{space 15}pais {c |}
{space 9}Guatemala  {c |}{col 21}{res}{space 2} .0145715{col 33}{space 2} .0790104{col 44}{space 1}    0.18{col 53}{space 3}0.854{col 61}{space 4}-.1404027{col 74}{space 3} .1695456
{txt}{space 7}El Salvador  {c |}{col 21}{res}{space 2} .4580328{col 33}{space 2} .0705161{col 44}{space 1}    6.50{col 53}{space 3}0.000{col 61}{space 4} .3197196{col 74}{space 3}  .596346
{txt}{space 10}Honduras  {c |}{col 21}{res}{space 2}  -.18274{col 33}{space 2} .0722195{col 44}{space 1}   -2.53{col 53}{space 3}0.011{col 61}{space 4}-.3243942{col 74}{space 3}-.0410857
{txt}{space 9}Nicaragua  {c |}{col 21}{res}{space 2} .0130983{col 33}{space 2} .0702144{col 44}{space 1}    0.19{col 53}{space 3}0.852{col 61}{space 4}-.1246231{col 74}{space 3} .1508197
{txt}{space 8}Costa Rica  {c |}{col 21}{res}{space 2} .8621271{col 33}{space 2} .0758744{col 44}{space 1}   11.36{col 53}{space 3}0.000{col 61}{space 4} .7133039{col 74}{space 3}  1.01095
{txt}{space 12}Panama  {c |}{col 21}{res}{space 2} .4034901{col 33}{space 2} .0751418{col 44}{space 1}    5.37{col 53}{space 3}0.000{col 61}{space 4} .2561038{col 74}{space 3} .5508764
{txt}{space 10}Colombia  {c |}{col 21}{res}{space 2}  -.38225{col 33}{space 2} .0704269{col 44}{space 1}   -5.43{col 53}{space 3}0.000{col 61}{space 4}-.5203882{col 74}{space 3}-.2441118
{txt}{space 11}Ecuador  {c |}{col 21}{res}{space 2} .4416938{col 33}{space 2} .0760801{col 44}{space 1}    5.81{col 53}{space 3}0.000{col 61}{space 4} .2924673{col 74}{space 3} .5909204
{txt}{space 11}Bolivia  {c |}{col 21}{res}{space 2} .2452513{col 33}{space 2} .0642912{col 44}{space 1}    3.81{col 53}{space 3}0.000{col 61}{space 4} .1191478{col 74}{space 3} .3713547
{txt}{space 14}Peru  {c |}{col 21}{res}{space 2} .5067641{col 33}{space 2} .0766983{col 44}{space 1}    6.61{col 53}{space 3}0.000{col 61}{space 4} .3563249{col 74}{space 3} .6572033
{txt}{space 10}Paraguay  {c |}{col 21}{res}{space 2} .5201214{col 33}{space 2} .0732259{col 44}{space 1}    7.10{col 53}{space 3}0.000{col 61}{space 4} .3764932{col 74}{space 3} .6637496
{txt}{space 13}Chile  {c |}{col 21}{res}{space 2} 1.053668{col 33}{space 2} .0808451{col 44}{space 1}   13.03{col 53}{space 3}0.000{col 61}{space 4} .8950955{col 74}{space 3} 1.212241
{txt}{space 11}Uruguay  {c |}{col 21}{res}{space 2} 1.507702{col 33}{space 2} .0727551{col 44}{space 1}   20.72{col 53}{space 3}0.000{col 61}{space 4} 1.364997{col 74}{space 3} 1.650407
{txt}{space 12}Brazil  {c |}{col 21}{res}{space 2} -.066399{col 33}{space 2} .0749998{col 44}{space 1}   -0.89{col 53}{space 3}0.376{col 61}{space 4}-.2135066{col 74}{space 3} .0807087
{txt}{space 9}Venezuela  {c |}{col 21}{res}{space 2} .6606388{col 33}{space 2} .0803214{col 44}{space 1}    8.22{col 53}{space 3}0.000{col 61}{space 4} .5030932{col 74}{space 3} .8181844
{txt}{space 9}Argentina  {c |}{col 21}{res}{space 2} .7926087{col 33}{space 2} .0760784{col 44}{space 1}   10.42{col 53}{space 3}0.000{col 61}{space 4} .6433855{col 74}{space 3} .9418319
{txt}Dominican Republic  {c |}{col 21}{res}{space 2} -.275362{col 33}{space 2} .0748443{col 44}{space 1}   -3.68{col 53}{space 3}0.000{col 61}{space 4}-.4221646{col 74}{space 3}-.1285594
{txt}{space 13}Haiti  {c |}{col 21}{res}{space 2}-.7263539{col 33}{space 2} .0757352{col 44}{space 1}   -9.59{col 53}{space 3}0.000{col 61}{space 4} -.874904{col 74}{space 3}-.5778038
{txt}{space 11}Jamaica  {c |}{col 21}{res}{space 2} .1065227{col 33}{space 2} .0660964{col 44}{space 1}    1.61{col 53}{space 3}0.107{col 61}{space 4}-.0231214{col 74}{space 3} .2361669
{txt}{space 12}Guyana  {c |}{col 21}{res}{space 2} .0884311{col 33}{space 2} .0965535{col 44}{space 1}    0.92{col 53}{space 3}0.360{col 61}{space 4}-.1009528{col 74}{space 3}  .277815
{txt}{space 12}Belize  {c |}{col 21}{res}{space 2}  .319438{col 33}{space 2} .0670424{col 44}{space 1}    4.76{col 53}{space 3}0.000{col 61}{space 4} .1879384{col 74}{space 3} .4509376
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} 2.582915{col 33}{space 2} .0913486{col 44}{space 1}   28.28{col 53}{space 3}0.000{col 61}{space 4} 2.403741{col 74}{space 3}  2.76209
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.                 margins, at(direct=1 rural=0 woman=1 agecohort=5 edr=3 quintall=5 m1=5 psa5=0) atmeans post // non-approver, vb exposure
{res}
{txt}Adjusted predictions

{col 1}{lalign 16:Number of strata}{col 9} = {res}{ralign 5:118}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:31,029}
{txt}{col 1}{lalign 16:Number of PSUs}{col 9} = {res}{ralign 5:1,726}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:29,269.078}
{txt}{col 1}Model VCE: {res:Linearized}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,608}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 4:At: }{space 0}{lalign 9:direct} = {res:{ralign 8:1}}
{lalign 4:}{space 0}{lalign 9:woman} = {res:{ralign 8:1}}
{lalign 4:}{space 0}{lalign 9:quintall} = {res:{ralign 8:5}}
{lalign 4:}{space 0}{lalign 9:agecohort} = {res:{ralign 8:5}}
{lalign 4:}{space 0}{lalign 9:edr} = {res:{ralign 8:3}}
{lalign 4:}{space 0}{lalign 9:rural} = {res:{ralign 8:0}}
{lalign 4:}{space 0}{lalign 9:m1} = {res:{ralign 8:5}}
{lalign 4:}{space 0}{lalign 9:ing4} = {res:{ralign 8:5.176212}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:psa5} = {res:{ralign 8:0}}
{lalign 4:}{space 0}{lalign 9:1.idio2} = {res:{ralign 8:.2003861}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:2.idio2} = {res:{ralign 8:.448288}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:3.idio2} = {res:{ralign 8:.3513259}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:1.soct2} = {res:{ralign 8:.158015}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:2.soct2} = {res:{ralign 8:.3663513}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:3.soct2} = {res:{ralign 8:.4756337}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:1.pais} = {res:{ralign 8:.0450387}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:2.pais} = {res:{ralign 8:.0444087}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:3.pais} = {res:{ralign 8:.0496217}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:4.pais} = {res:{ralign 8:.0474731}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:5.pais} = {res:{ralign 8:.0479337}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:6.pais} = {res:{ralign 8:.0471141}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:7.pais} = {res:{ralign 8:.0474424}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:8.pais} = {res:{ralign 8:.0463156}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:9.pais} = {res:{ralign 8:.0453287}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:10.pais} = {res:{ralign 8:.0460812}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:11.pais} = {res:{ralign 8:.0435272}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:12.pais} = {res:{ralign 8:.0415649}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:13.pais} = {res:{ralign 8:.0396103}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:14.pais} = {res:{ralign 8:.0477575}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:15.pais} = {res:{ralign 8:.0483742}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:16.pais} = {res:{ralign 8:.0468071}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:17.pais} = {res:{ralign 8:.0439952}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:21.pais} = {res:{ralign 8:.0482479}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:22.pais} = {res:{ralign 8:.040199}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:23.pais} = {res:{ralign 8:.0434062}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:24.pais} = {res:{ralign 8:.0431845}} {txt:(mean)}
{lalign 4:}{space 0}{lalign 9:26.pais} = {res:{ralign 8:.0465684}} {txt:(mean)}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}_cons {c |}{col 14}{res}{space 2} 1.530694{col 26}{space 2}  .055309{col 37}{space 1}   27.68{col 46}{space 3}0.000{col 54}{space 4} 1.422208{col 67}{space 3} 1.639179
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.                 eststo m4
{txt}
{com}.         coefplot m1 m2 m3 m4
{res}{txt}
{com}. restore
{txt}
{com}. 
. 
. *** A20
. *DALP data set for merging
. preserve
{txt}
{com}.         use "../data/countrylevel_20130907.dta", clear
{txt}
{com}.         tab b6

         {txt}B6 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
     1.3125 {c |}{res}          1        1.14        1.14
{txt}        1.4 {c |}{res}          1        1.14        2.27
{txt}   1.461538 {c |}{res}          1        1.14        3.41
{txt}        1.5 {c |}{res}          2        2.27        5.68
{txt}   1.590909 {c |}{res}          1        1.14        6.82
{txt}        1.6 {c |}{res}          2        2.27        9.09
{txt}   1.923077 {c |}{res}          1        1.14       10.23
{txt}   2.066667 {c |}{res}          1        1.14       11.36
{txt}   2.117647 {c |}{res}          1        1.14       12.50
{txt}   2.230769 {c |}{res}          1        1.14       13.64
{txt}     2.2625 {c |}{res}          1        1.14       14.77
{txt}   2.266667 {c |}{res}          1        1.14       15.91
{txt}   2.285714 {c |}{res}          1        1.14       17.05
{txt}   2.333333 {c |}{res}          3        3.41       20.45
{txt}        2.4 {c |}{res}          2        2.27       22.73
{txt}     2.4375 {c |}{res}          2        2.27       25.00
{txt}   2.533333 {c |}{res}          1        1.14       26.14
{txt}   2.538461 {c |}{res}          1        1.14       27.27
{txt}   2.555556 {c |}{res}          1        1.14       28.41
{txt}   2.705882 {c |}{res}          2        2.27       30.68
{txt}        2.8 {c |}{res}          1        1.14       31.82
{txt}   2.818182 {c |}{res}          1        1.14       32.95
{txt}   2.846154 {c |}{res}          1        1.14       34.09
{txt}   2.888889 {c |}{res}          1        1.14       35.23
{txt}   2.916667 {c |}{res}          1        1.14       36.36
{txt}   2.925926 {c |}{res}          1        1.14       37.50
{txt}   2.928571 {c |}{res}          1        1.14       38.64
{txt}   2.941176 {c |}{res}          1        1.14       39.77
{txt}          3 {c |}{res}          5        5.68       45.45
{txt}   3.055556 {c |}{res}          1        1.14       46.59
{txt}   3.111111 {c |}{res}          1        1.14       47.73
{txt}   3.166667 {c |}{res}          2        2.27       50.00
{txt}     3.1875 {c |}{res}          1        1.14       51.14
{txt}   3.227273 {c |}{res}          1        1.14       52.27
{txt}   3.230769 {c |}{res}          1        1.14       53.41
{txt}       3.25 {c |}{res}          2        2.27       55.68
{txt}   3.263158 {c |}{res}          1        1.14       56.82
{txt}   3.285714 {c |}{res}          1        1.14       57.95
{txt}   3.294118 {c |}{res}          1        1.14       59.09
{txt}        3.3 {c |}{res}          1        1.14       60.23
{txt}   3.333333 {c |}{res}          3        3.41       63.64
{txt}   3.368421 {c |}{res}          1        1.14       64.77
{txt}        3.4 {c |}{res}          1        1.14       65.91
{txt}   3.416667 {c |}{res}          2        2.27       68.18
{txt}   3.428571 {c |}{res}          1        1.14       69.32
{txt}   3.461539 {c |}{res}          2        2.27       71.59
{txt}   3.466667 {c |}{res}          1        1.14       72.73
{txt}   3.474359 {c |}{res}          1        1.14       73.86
{txt}        3.5 {c |}{res}          3        3.41       77.27
{txt}   3.571429 {c |}{res}          1        1.14       78.41
{txt}   3.583333 {c |}{res}          2        2.27       80.68
{txt}   3.590909 {c |}{res}          1        1.14       81.82
{txt}   3.615385 {c |}{res}          1        1.14       82.95
{txt}      3.625 {c |}{res}          1        1.14       84.09
{txt}   3.636364 {c |}{res}          1        1.14       85.23
{txt}   3.666667 {c |}{res}          5        5.68       90.91
{txt}   3.692308 {c |}{res}          1        1.14       92.05
{txt}   3.727273 {c |}{res}          1        1.14       93.18
{txt}   3.733333 {c |}{res}          1        1.14       94.32
{txt}   3.769231 {c |}{res}          1        1.14       95.45
{txt}        3.8 {c |}{res}          1        1.14       96.59
{txt}        3.9 {c |}{res}          1        1.14       97.73
{txt}   3.923077 {c |}{res}          1        1.14       98.86
{txt}   3.933333 {c |}{res}          1        1.14      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         88      100.00
{txt}
{com}.         gen pais=.
{txt}(88 missing values generated)

{com}.                 replace pais=1 if country=="Mexico"
{txt}(1 real change made)

{com}.                 replace pais=2 if country=="Guatemala"
{txt}(1 real change made)

{com}.                 replace pais=3 if country=="El Salvador"
{txt}(1 real change made)

{com}.                 replace pais=4 if country=="Honduras"
{txt}(1 real change made)

{com}.                 replace pais=5 if country=="Nicaragua"
{txt}(1 real change made)

{com}.                 replace pais=6 if country=="Costa Rica"
{txt}(1 real change made)

{com}.                 replace pais=7 if country=="Panama"
{txt}(1 real change made)

{com}.                 replace pais=8 if country=="Colombia"
{txt}(1 real change made)

{com}.                 replace pais=9 if country=="Ecuador"
{txt}(1 real change made)

{com}.                 replace pais=10 if country=="Bolivia"
{txt}(1 real change made)

{com}.                 replace pais=11 if country=="Peru"
{txt}(1 real change made)

{com}.                 replace pais=12 if country=="Paraguay"
{txt}(1 real change made)

{com}.                 replace pais=13 if country=="Chile"
{txt}(1 real change made)

{com}.                 replace pais=14 if country=="Uruguay"
{txt}(1 real change made)

{com}.                 replace pais=15 if country=="Brazil"
{txt}(1 real change made)

{com}.                 replace pais=16 if country=="Venezuela"
{txt}(1 real change made)

{com}.                 replace pais=17 if country=="Argentina"
{txt}(1 real change made)

{com}.                 replace pais=21 if country=="Dom. Rep." 
{txt}(1 real change made)

{com}.                 replace pais=23 if country=="Jamaica"
{txt}(1 real change made)

{com}.         drop if missing(pais) // Belize, Guyana, Haiti missing from DALP
{txt}(69 observations deleted)

{com}.         rename b6 vb_dalp
{res}{txt}
{com}.         keep pais vb_dalp
{txt}
{com}.         save "../data/dalp.dta", replace
{txt}{p 0 4 2}
(file {bf}
../data/dalp.dta{rm}
not found)
{p_end}
{p 0 4 2}
file {bf}
../data/dalp.dta{rm}
saved
{p_end}

{com}. restore
{txt}
{com}.         
. *** Figure
. merge m:m pais using "../data/dalp.dta", gen(merge_dalp) // merging dalp data set
{res}
{txt}{col 5}Result{col 33}Number of obs
{col 5}{hline 41}
{col 5}Not matched{col 30}{res}          73,171
{txt}{col 9}from master{col 30}{res}          73,171{txt}  (merge_dalp==1)
{col 9}from using{col 30}{res}               0{txt}  (merge_dalp==2)

{col 5}Matched{col 30}{res}         237,083{txt}  (merge_dalp==3)
{col 5}{hline 41}

{com}. gen dalp=vb_dalp
{txt}(73,171 missing values generated)

{com}. rescale dalp 0 1
{txt}(237,083 real changes made)

{com}. mixed trustel i.indirect##c.dalp woman quintall agecohort edr rural m1 if wave==2014 [pweight = weight1500] || pais: 
{res}
{txt}Obtaining starting values by EM ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-52617.504}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-52617.504}  (backed up)
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 53}Number of obs{col 69} = {res} 28,452
{txt}Group variable: {res}pais{col 53}{txt}Number of groups{col 69} = {res}     19
{txt}{col 53}Obs per group:
{col 66}min = {res}  1,307
{txt}{col 66}avg = {res}1,497.5
{txt}{col 66}max = {res}  2,860
{col 53}{txt}Wald chi2({res}9{txt}){col 69} = {res} 708.57
{txt}Log pseudolikelihood = {res}-52617.504{col 53}{txt}Prob > chi2{col 69} = {res} 0.0000

{txt}{ralign 81:(Std. err. adjusted for {res:19} clusters in {res:pais})}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}        trustel{col 17}{c |} Coefficient{col 29}  std. err.{col 41}      z{col 49}   P>|z|{col 57}     [95% con{col 70}f. interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}1.indirect {c |}{col 17}{res}{space 2}-.3160875{col 29}{space 2} .1529511{col 40}{space 1}   -2.07{col 49}{space 3}0.039{col 57}{space 4}-.6158662{col 70}{space 3}-.0163088
{txt}{space 11}dalp {c |}{col 17}{res}{space 2}-1.355184{col 29}{space 2} .3869139{col 40}{space 1}   -3.50{col 49}{space 3}0.000{col 57}{space 4}-2.113522{col 70}{space 3} -.596847
{txt}{space 15} {c |}
indirect#c.dalp {c |}
{space 13}1  {c |}{col 17}{res}{space 2} .1536432{col 29}{space 2} .2546175{col 40}{space 1}    0.60{col 49}{space 3}0.546{col 57}{space 4}-.3453979{col 70}{space 3} .6526844
{txt}{space 15} {c |}
{space 10}woman {c |}{col 17}{res}{space 2}-.0528753{col 29}{space 2} .0185675{col 40}{space 1}   -2.85{col 49}{space 3}0.004{col 57}{space 4}-.0892669{col 70}{space 3}-.0164836
{txt}{space 7}quintall {c |}{col 17}{res}{space 2}-.0307676{col 29}{space 2} .0472732{col 40}{space 1}   -0.65{col 49}{space 3}0.515{col 57}{space 4}-.1234212{col 70}{space 3} .0618861
{txt}{space 6}agecohort {c |}{col 17}{res}{space 2} .3035552{col 29}{space 2} .0994347{col 40}{space 1}    3.05{col 49}{space 3}0.002{col 57}{space 4} .1086667{col 70}{space 3} .4984437
{txt}{space 12}edr {c |}{col 17}{res}{space 2}  .190631{col 29}{space 2} .0904316{col 40}{space 1}    2.11{col 49}{space 3}0.035{col 57}{space 4} .0133883{col 70}{space 3} .3678737
{txt}{space 10}rural {c |}{col 17}{res}{space 2} .2039426{col 29}{space 2} .0480804{col 40}{space 1}    4.24{col 49}{space 3}0.000{col 57}{space 4} .1097068{col 70}{space 3} .2981784
{txt}{space 13}m1 {c |}{col 17}{res}{space 2} 2.680522{col 29}{space 2} .2487663{col 40}{space 1}   10.78{col 49}{space 3}0.000{col 57}{space 4} 2.192949{col 70}{space 3} 3.168095
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 3.023287{col 29}{space 2}  .287075{col 40}{space 1}   10.53{col 49}{space 3}0.000{col 57}{space 4} 2.460631{col 70}{space 3} 3.585944
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}pais{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33} .2432463{col 44} .0623565{col 58} .1471769{col 70} .4020249
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 3.000871{col 44} .1137379{col 58} 2.786027{col 70} 3.232282
{txt}{hline 29}{c BT}{hline 48}

{p 0 9 2}Warning: Sampling weights were 
specified only at the first level 
in a multilevel model. If these weights are 
indicative of overall and not conditional 
inclusion probabilities, then 
{help mixed##sampling:results may be biased}.
{p_end}

{com}.         outreg2 using a20.doc, dec(2) replace ctitle(Indirect)
{txt}{stata `"shellout using `"a20.doc"'"':a20.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a20.txt""':seeout}

{com}.         margins, dydx(indirect) at(dalp=(0(.25)1))
{res}
{txt}{col 1}Average marginal effects{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:28,452}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.indirect}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 4:dalp} = {res:{ralign 3:0}}
{lalign 7:2._at: }{space 0}{lalign 4:dalp} = {res:{ralign 3:.25}}
{lalign 7:3._at: }{space 0}{lalign 4:dalp} = {res:{ralign 3:.5}}
{lalign 7:4._at: }{space 0}{lalign 4:dalp} = {res:{ralign 3:.75}}
{lalign 7:5._at: }{space 0}{lalign 4:dalp} = {res:{ralign 3:1}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.indirect  {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.indirect   {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.3160875{col 26}{space 2} .1529511{col 37}{space 1}   -2.07{col 46}{space 3}0.039{col 54}{space 4}-.6158662{col 67}{space 3}-.0163088
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.2776767{col 26}{space 2} .0965206{col 37}{space 1}   -2.88{col 46}{space 3}0.004{col 54}{space 4}-.4668535{col 67}{space 3}-.0884998
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.2392658{col 26}{space 2} .0578114{col 37}{space 1}   -4.14{col 46}{space 3}0.000{col 54}{space 4} -.352574{col 67}{space 3}-.1259577
{txt}{space 10}4  {c |}{col 14}{res}{space 2} -.200855{col 26}{space 2} .0739719{col 37}{space 1}   -2.72{col 46}{space 3}0.007{col 54}{space 4}-.3458374{col 67}{space 3}-.0558727
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.1624442{col 26}{space 2} .1253208{col 37}{space 1}   -1.30{col 46}{space 3}0.195{col 54}{space 4}-.4080685{col 67}{space 3}   .08318
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) ///
>                 xtitle("Inducing votes with targeted benefits, DALP") ///
>                 ytitle("Effects on Trust in Elections") ///
>                 title(Indirect Exposure to Vote Buying)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:dalp}{p_end}
{res}{txt}
{com}.         graph save Graph "fig1dalp_indirect.gph", replace                               
{res}{txt}file {bf:fig1dalp_indirect.gph} saved

{com}. mixed trustel i.direct##c.dalp woman quintall agecohort edr rural m1 if wave==2014 [pweight = weight1500] || pais: 
{res}
{txt}Obtaining starting values by EM ...
{res}
{txt}Performing gradient-based optimization: 
{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res: -52828.17}  
Iteration 1:{space 2}Log pseudolikelihood = {res: -52828.17}  (backed up)
{res}
{txt}Computing standard errors ...
{res}
{txt}Mixed-effects regression{col 53}Number of obs{col 69} = {res} 28,558
{txt}Group variable: {res}pais{col 53}{txt}Number of groups{col 69} = {res}     19
{txt}{col 53}Obs per group:
{col 66}min = {res}  1,314
{txt}{col 66}avg = {res}1,503.1
{txt}{col 66}max = {res}  2,862
{col 53}{txt}Wald chi2({res}9{txt}){col 69} = {res} 830.52
{txt}Log pseudolikelihood = {res} -52828.17{col 53}{txt}Prob > chi2{col 69} = {res} 0.0000

{txt}{ralign 79:(Std. err. adjusted for {res:19} clusters in {res:pais})}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}      trustel{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}1.direct {c |}{col 15}{res}{space 2}-.1119853{col 27}{space 2} .2369792{col 38}{space 1}   -0.47{col 47}{space 3}0.637{col 55}{space 4} -.576456{col 68}{space 3} .3524854
{txt}{space 9}dalp {c |}{col 15}{res}{space 2}-1.342686{col 27}{space 2}  .388044{col 38}{space 1}   -3.46{col 47}{space 3}0.001{col 55}{space 4}-2.103238{col 68}{space 3}-.5821338
{txt}{space 13} {c |}
direct#c.dalp {c |}
{space 11}1  {c |}{col 15}{res}{space 2}-.1140797{col 27}{space 2} .3976786{col 38}{space 1}   -0.29{col 47}{space 3}0.774{col 55}{space 4}-.8935154{col 68}{space 3}  .665356
{txt}{space 13} {c |}
{space 8}woman {c |}{col 15}{res}{space 2}-.0530667{col 27}{space 2} .0187934{col 38}{space 1}   -2.82{col 47}{space 3}0.005{col 55}{space 4}-.0899012{col 68}{space 3}-.0162323
{txt}{space 5}quintall {c |}{col 15}{res}{space 2}-.0230642{col 27}{space 2} .0477152{col 38}{space 1}   -0.48{col 47}{space 3}0.629{col 55}{space 4}-.1165843{col 68}{space 3} .0704558
{txt}{space 4}agecohort {c |}{col 15}{res}{space 2} .3112315{col 27}{space 2}  .097625{col 38}{space 1}    3.19{col 47}{space 3}0.001{col 55}{space 4} .1198899{col 68}{space 3} .5025731
{txt}{space 10}edr {c |}{col 15}{res}{space 2} .1758855{col 27}{space 2} .0887551{col 38}{space 1}    1.98{col 47}{space 3}0.048{col 55}{space 4} .0019288{col 68}{space 3} .3498422
{txt}{space 8}rural {c |}{col 15}{res}{space 2}  .200351{col 27}{space 2} .0474193{col 38}{space 1}    4.23{col 47}{space 3}0.000{col 55}{space 4} .1074109{col 68}{space 3} .2932911
{txt}{space 11}m1 {c |}{col 15}{res}{space 2}  2.67958{col 27}{space 2} .2491288{col 38}{space 1}   10.76{col 47}{space 3}0.000{col 55}{space 4} 2.191296{col 68}{space 3} 3.167863
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 3.006141{col 27}{space 2} .2895809{col 38}{space 1}   10.38{col 47}{space 3}0.000{col 55}{space 4} 2.438573{col 68}{space 3} 3.573709
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 30}{c |}{col 34}{col 46}Robust{col 63}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{res}pais{txt}: Identity{col 30}{c |}
{space 18}var(_cons) {c |}{res}{col 33} .2476986{col 44} .0627808{col 58} .1507235{col 70} .4070669
{txt}{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 3.003813{col 44} .1125816{col 58} 2.791067{col 70} 3.232775
{txt}{hline 29}{c BT}{hline 48}

{p 0 9 2}Warning: Sampling weights were 
specified only at the first level 
in a multilevel model. If these weights are 
indicative of overall and not conditional 
inclusion probabilities, then 
{help mixed##sampling:results may be biased}.
{p_end}

{com}.         outreg2 using a20.doc, dec(2) append ctitle(Direct)
{txt}{stata `"shellout using `"a20.doc"'"':a20.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "a20.txt""':seeout}

{com}.         margins, dydx(direct) at(dalp=(0(.25)1)) 
{res}
{txt}{col 1}Average marginal effects{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:28,558}
{txt}{col 1}Model VCE: {res:Robust}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.direct}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 4:dalp} = {res:{ralign 3:0}}
{lalign 7:2._at: }{space 0}{lalign 4:dalp} = {res:{ralign 3:.25}}
{lalign 7:3._at: }{space 0}{lalign 4:dalp} = {res:{ralign 3:.5}}
{lalign 7:4._at: }{space 0}{lalign 4:dalp} = {res:{ralign 3:.75}}
{lalign 7:5._at: }{space 0}{lalign 4:dalp} = {res:{ralign 3:1}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.direct    {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.direct     {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.1119853{col 26}{space 2} .2369792{col 37}{space 1}   -0.47{col 46}{space 3}0.637{col 54}{space 4} -.576456{col 67}{space 3} .3524854
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.1405052{col 26}{space 2}  .146993{col 37}{space 1}   -0.96{col 46}{space 3}0.339{col 54}{space 4}-.4286062{col 67}{space 3} .1475957
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1690251{col 26}{space 2}  .082603{col 37}{space 1}   -2.05{col 46}{space 3}0.041{col 54}{space 4}-.3309241{col 67}{space 3}-.0071262
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.1975451{col 26}{space 2} .1086652{col 37}{space 1}   -1.82{col 46}{space 3}0.069{col 54}{space 4}-.4105249{col 67}{space 3} .0154348
{txt}{space 10}5  {c |}{col 14}{res}{space 2} -.226065{col 26}{space 2} .1912107{col 37}{space 1}   -1.18{col 46}{space 3}0.237{col 54}{space 4} -.600831{col 67}{space 3}  .148701
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) ///
>                 xtitle("Inducing votes with targeted benefits, DALP") ///
>                 ytitle("Effects on Trust in Elections") ///
>                 title(Direct Exposure to Vote Buying)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:dalp}{p_end}
{res}{txt}
{com}.         graph save Graph "fig1dalp_direct.gph", replace
{res}{txt}file {bf:fig1dalp_direct.gph} saved

{com}. graph combine fig1dalp_direct.gph fig1dalp_indirect.gph, ysize(3) scale(1.4)
{res}{txt}
{com}. graph save a20.gph, replace
{res}{txt}file {bf:a20.gph} saved

{com}. 
. 
. **************** A23
. tab pais if !missing(trustel, indirect, approval, woman, quintall, agecohort, edr, rural, m1)

                         {txt}Country {c |}      Freq.     Percent        Cum.
{hline 33}{c +}{hline 35}
                          Mexico {c |}{res}      1,461       17.65       17.65
{txt}                       Guatemala {c |}{res}      1,420       17.16       34.81
{txt}                            Peru {c |}{res}      1,405       16.97       51.78
{txt}                        Paraguay {c |}{res}      1,371       16.56       68.35
{txt}              Dominican Republic {c |}{res}      1,396       16.87       85.21
{txt}                         Jamaica {c |}{res}      1,224       14.79      100.00
{txt}{hline 33}{c +}{hline 35}
                           Total {c |}{res}      8,277      100.00
{txt}
{com}. tab pais if !missing(trustel, direct, approval, woman, quintall, agecohort, edr, rural, m1) // MEX, GTM, PER, PRY, DOM, JAM

                         {txt}Country {c |}      Freq.     Percent        Cum.
{hline 33}{c +}{hline 35}
                          Mexico {c |}{res}      1,464       17.53       17.53
{txt}                       Guatemala {c |}{res}      1,427       17.08       34.61
{txt}                            Peru {c |}{res}      1,413       16.92       51.53
{txt}                        Paraguay {c |}{res}      1,383       16.56       68.08
{txt}              Dominican Republic {c |}{res}      1,408       16.86       84.94
{txt}                         Jamaica {c |}{res}      1,258       15.06      100.00
{txt}{hline 33}{c +}{hline 35}
                           Total {c |}{res}      8,353      100.00
{txt}
{com}.         fre pais // 1, 2, 11, 12, 21, 23
{res}
{txt}pais {hline 2} Country
{txt}{hline 43}{hline 1}{c TT}{hline 44}
{txt}        {txt}                                    {c |}      Freq.    Percent      Valid       Cum.
{txt}{hline 43}{hline 1}{c +}{hline 44}
{txt}Valid   1  Mexico                           {c |}{res}      12476       4.02       4.02       4.02
{txt}        2  Guatemala                        {c |}{res}      12395       4.00       4.00       8.02
{txt}        3  El Salvador                      {c |}{res}      12488       4.03       4.03      12.04
{txt}        4  Honduras                         {c |}{res}      12612       4.07       4.07      16.11
{txt}        5  Nicaragua                        {c |}{res}      12607       4.06       4.06      20.17
{txt}        6  Costa Rica                       {c |}{res}      12046       3.88       3.88      24.05
{txt}        7  Panama                           {c |}{res}      12455       4.01       4.01      28.07
{txt}        8  Colombia                         {c |}{res}      12213       3.94       3.94      32.00
{txt}        9  Ecuador                          {c |}{res}      17991       5.80       5.80      37.80
{txt}        10 Bolivia                          {c |}{res}      21569       6.95       6.95      44.75
{txt}        11 Peru                             {c |}{res}      11668       3.76       3.76      48.52
{txt}        12 Paraguay                         {c |}{res}       9888       3.19       3.19      51.70
{txt}        13 Chile                            {c |}{res}      11414       3.68       3.68      55.38
{txt}        14 Uruguay                          {c |}{res}      10319       3.33       3.33      58.71
{txt}        15 Brazil                           {c |}{res}      11222       3.62       3.62      62.32
{txt}        16 Venezuela                        {c |}{res}       9068       2.92       2.92      65.25
{txt}        17 Argentina                        {c |}{res}       8976       2.89       2.89      68.14
{txt}        21 Dominican Republic               {c |}{res}      15047       4.85       4.85      72.99
{txt}        22 Haiti                            {c |}{res}      10482       3.38       3.38      76.37
{txt}        23 Jamaica                          {c |}{res}      10629       3.43       3.43      79.79
{txt}        24 Guyana                           {c |}{res}      10271       3.31       3.31      83.10
{txt}        25 Trinidad & Tobago                {c |}{res}       7212       2.32       2.32      85.43
{txt}        26 Belize                           {c |}{res}       6101       1.97       1.97      87.40
{txt}        27 Suriname                         {c |}{res}       7006       2.26       2.26      89.65
{txt}        28 Bahamas                          {c |}{res}       3429       1.11       1.11      90.76
{txt}        29 Barbados                         {c |}{res}       3828       1.23       1.23      91.99
{txt}        30 Grenada                          {c |}{res}       1004       0.32       0.32      92.32
{txt}        31 Saint Lucia                      {c |}{res}       1019       0.33       0.33      92.65
{txt}        32 Dominica                         {c |}{res}       1016       0.33       0.33      92.97
{txt}        34 Saint Vincent and the Grenadines {c |}{res}       1017       0.33       0.33      93.30
{txt}        35 Saint Kitts and Nevis            {c |}{res}       1008       0.32       0.32      93.63
{txt}        40 United States                    {c |}{res}       9609       3.10       3.10      96.72
{txt}        41 Canada                           {c |}{res}      10169       3.28       3.28     100.00
{txt}        Total                               {c |}{res}     310254     100.00     100.00           
{txt}{hline 43}{hline 1}{c BT}{hline 44}

{com}. *** quintiles of approval
. sum approval if !missing(trustel, indirect, woman, quintall, agecohort, edr, rural, m1), detail // 25%=0, 50%=.25, 75%=.5, 100%=1

                          {txt}approval
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}      8,277
{txt}25%    {res}        0              0       {txt}Sum of wgt. {res}      8,277

{txt}50%    {res}      .25                      {txt}Mean          {res}  .330917
                        {txt}Largest       Std. dev.     {res} .2819417
{txt}75%    {res}       .5              1
{txt}90%    {res}      .75              1       {txt}Variance      {res} .0794911
{txt}95%    {res}        1              1       {txt}Skewness      {res} .7174083
{txt}99%    {res}        1              1       {txt}Kurtosis      {res} 2.867583
{txt}
{com}.         recode approval (0=0 "25%")(.25=1 "50%")(.5=2 "75%")(.75 1=3 "100%"), gen(approval_percentiles)
{txt}(7,815 differences between {bf:approval} and {bf:approval_percentiles})

{com}.         lab var approval_percentiles "Approval of Vote Buying, Percentiles"
{txt}
{com}.         
. mixed trustel i.indirect##c.approval_percentiles woman quintall agecohort edr rural m1 if pais==1
{res}
{txt}Mixed-effects ML regression{col 57}Number of obs{col 70} = {res} 1,461
{col 57}{txt}Wald chi2({res}9{txt}){col 70} = {res}179.07
{txt}Log likelihood = {res}-2897.4897{col 57}{txt}Prob > chi2{col 70} = {res}0.0000

{txt}{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                        trustel{col 33}{c |} Coefficient{col 45}  Std. err.{col 57}      z{col 65}   P>|z|{col 73}     [95% con{col 86}f. interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}1.indirect {c |}{col 33}{res}{space 2}-.0394234{col 45}{space 2} .1499478{col 56}{space 1}   -0.26{col 65}{space 3}0.793{col 73}{space 4}-.3333158{col 86}{space 3}  .254469
{txt}{space 11}approval_percentiles {c |}{col 33}{res}{space 2} .1493065{col 45}{space 2} .0623937{col 56}{space 1}    2.39{col 65}{space 3}0.017{col 73}{space 4} .0270171{col 86}{space 3}  .271596
{txt}{space 31} {c |}
indirect#c.approval_percentiles {c |}
{space 29}1  {c |}{col 33}{res}{space 2}-.1619282{col 45}{space 2} .1044789{col 56}{space 1}   -1.55{col 65}{space 3}0.121{col 73}{space 4}-.3667032{col 86}{space 3} .0428467
{txt}{space 31} {c |}
{space 26}woman {c |}{col 33}{res}{space 2}-.1669012{col 45}{space 2} .0932951{col 56}{space 1}   -1.79{col 65}{space 3}0.074{col 73}{space 4}-.3497562{col 86}{space 3} .0159538
{txt}{space 23}quintall {c |}{col 33}{res}{space 2} .1222684{col 45}{space 2} .1396665{col 56}{space 1}    0.88{col 65}{space 3}0.381{col 73}{space 4}-.1514729{col 86}{space 3} .3960097
{txt}{space 22}agecohort {c |}{col 33}{res}{space 2} .8421489{col 45}{space 2} .1531509{col 56}{space 1}    5.50{col 65}{space 3}0.000{col 73}{space 4} .5419786{col 86}{space 3} 1.142319
{txt}{space 28}edr {c |}{col 33}{res}{space 2}-.6566584{col 45}{space 2} .2301915{col 56}{space 1}   -2.85{col 65}{space 3}0.004{col 73}{space 4}-1.107825{col 86}{space 3}-.2054913
{txt}{space 26}rural {c |}{col 33}{res}{space 2} .1041616{col 45}{space 2} .1157034{col 56}{space 1}    0.90{col 65}{space 3}0.368{col 73}{space 4}-.1226129{col 86}{space 3} .3309361
{txt}{space 29}m1 {c |}{col 33}{res}{space 2} 2.254304{col 45}{space 2} .2329782{col 56}{space 1}    9.68{col 65}{space 3}0.000{col 73}{space 4} 1.797675{col 86}{space 3} 2.710933
{txt}{space 26}_cons {c |}{col 33}{res}{space 2} 2.336955{col 45}{space 2} .2714737{col 56}{space 1}    8.61{col 65}{space 3}0.000{col 73}{space 4} 1.804876{col 86}{space 3} 2.869033
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}Std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 3.091233{col 44} .1143725{col 58} 2.875002{col 70} 3.323727
{txt}{hline 29}{c BT}{hline 48}

{com}.         margins, dydx(indirect) at(approval_percentiles=(0 1 2 3))
{res}
{txt}{col 1}Average marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,461}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.indirect}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:3}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.indirect  {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.indirect   {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0394234{col 26}{space 2} .1499478{col 37}{space 1}   -0.26{col 46}{space 3}0.793{col 54}{space 4}-.3333158{col 67}{space 3}  .254469
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.2013516{col 26}{space 2} .1019538{col 37}{space 1}   -1.97{col 46}{space 3}0.048{col 54}{space 4}-.4011774{col 67}{space 3}-.0015259
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.3632799{col 26}{space 2} .1419031{col 37}{space 1}   -2.56{col 46}{space 3}0.010{col 54}{space 4}-.6414048{col 67}{space 3} -.085155
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.5252081{col 26}{space 2} .2273985{col 37}{space 1}   -2.31{col 46}{space 3}0.021{col 54}{space 4} -.970901{col 67}{space 3}-.0795152
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) title("Indirect") ytitle("Effects on Trust in Elections")
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:approval_percentiles}{p_end}
{res}{txt}
{com}.         graph save Graph "a23_indirect_MEX.gph", replace                                
{res}{txt}file {bf:a23_indirect_MEX.gph} saved

{com}. mixed trustel i.direct##c.approval_percentiles woman quintall agecohort edr rural m1 if pais==1
{res}
{txt}Mixed-effects ML regression{col 57}Number of obs{col 70} = {res} 1,464
{col 57}{txt}Wald chi2({res}9{txt}){col 70} = {res}188.77
{txt}Log likelihood = {res}-2899.8716{col 57}{txt}Prob > chi2{col 70} = {res}0.0000

{txt}{hline 30}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                      trustel{col 31}{c |} Coefficient{col 43}  Std. err.{col 55}      z{col 63}   P>|z|{col 71}     [95% con{col 84}f. interval]
{hline 30}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}1.direct {c |}{col 31}{res}{space 2} .1746025{col 43}{space 2} .1762873{col 54}{space 1}    0.99{col 63}{space 3}0.322{col 71}{space 4}-.1709143{col 84}{space 3} .5201192
{txt}{space 9}approval_percentiles {c |}{col 31}{res}{space 2} .1647585{col 43}{space 2} .0563551{col 54}{space 1}    2.92{col 63}{space 3}0.003{col 71}{space 4} .0543045{col 84}{space 3} .2752124
{txt}{space 29} {c |}
direct#c.approval_percentiles {c |}
{space 27}1  {c |}{col 31}{res}{space 2}-.3765253{col 43}{space 2} .1241095{col 54}{space 1}   -3.03{col 63}{space 3}0.002{col 71}{space 4}-.6197755{col 84}{space 3}-.1332751
{txt}{space 29} {c |}
{space 24}woman {c |}{col 31}{res}{space 2}-.1716344{col 43}{space 2} .0929842{col 54}{space 1}   -1.85{col 63}{space 3}0.065{col 71}{space 4}  -.35388{col 84}{space 3} .0106113
{txt}{space 21}quintall {c |}{col 31}{res}{space 2} .1162026{col 43}{space 2} .1388907{col 54}{space 1}    0.84{col 63}{space 3}0.403{col 71}{space 4}-.1560182{col 84}{space 3} .3884235
{txt}{space 20}agecohort {c |}{col 31}{res}{space 2} .8810315{col 43}{space 2} .1524476{col 54}{space 1}    5.78{col 63}{space 3}0.000{col 71}{space 4} .5822397{col 84}{space 3} 1.179823
{txt}{space 26}edr {c |}{col 31}{res}{space 2}-.6500153{col 43}{space 2} .2294731{col 54}{space 1}   -2.83{col 63}{space 3}0.005{col 71}{space 4}-1.099774{col 84}{space 3}-.2002563
{txt}{space 24}rural {c |}{col 31}{res}{space 2} .0912504{col 43}{space 2} .1154733{col 54}{space 1}    0.79{col 63}{space 3}0.429{col 71}{space 4}-.1350732{col 84}{space 3} .3175739
{txt}{space 27}m1 {c |}{col 31}{res}{space 2} 2.263287{col 43}{space 2} .2319653{col 54}{space 1}    9.76{col 63}{space 3}0.000{col 71}{space 4} 1.808644{col 84}{space 3} 2.717931
{txt}{space 24}_cons {c |}{col 31}{res}{space 2}  2.28183{col 43}{space 2} .2691992{col 54}{space 1}    8.48{col 63}{space 3}0.000{col 71}{space 4} 1.754209{col 84}{space 3} 2.809451
{txt}{hline 30}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}Std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 3.076203{col 44} .1136997{col 58} 2.861236{col 70} 3.307321
{txt}{hline 29}{c BT}{hline 48}

{com}.         margins, dydx(direct) at(approval_percentiles=(0 1 2 3))
{res}
{txt}{col 1}Average marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,464}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.direct}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:3}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.direct    {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.direct     {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .1746025{col 26}{space 2} .1762873{col 37}{space 1}    0.99{col 46}{space 3}0.322{col 54}{space 4}-.1709143{col 67}{space 3} .5201192
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.2019228{col 26}{space 2} .1217213{col 37}{space 1}   -1.66{col 46}{space 3}0.097{col 54}{space 4}-.4404922{col 67}{space 3} .0366465
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.5784482{col 26}{space 2} .1713514{col 37}{space 1}   -3.38{col 46}{space 3}0.001{col 54}{space 4}-.9142907{col 67}{space 3}-.2426056
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.9549735{col 26}{space 2} .2733366{col 37}{space 1}   -3.49{col 46}{space 3}0.000{col 54}{space 4}-1.490703{col 67}{space 3}-.4192437
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) title("Direct") ///
>                 ytitle("Effects on Trust in Elections")
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:approval_percentiles}{p_end}
{res}{txt}
{com}.         graph save Graph "a23_direct_MEX.gph", replace
{res}{txt}file {bf:a23_direct_MEX.gph} saved

{com}. graph combine a23_direct_MEX.gph a23_indirect_MEX.gph, xsize(8) ysize(4) title("Mexico")
{res}{txt}
{com}.         graph save Graph "a23_MEX.gph", replace
{res}{txt}file {bf:a23_MEX.gph} saved

{com}.         
.         
. mixed trustel i.indirect##c.approval_percentiles woman quintall agecohort edr rural m1 if pais==2
{res}
{txt}Mixed-effects ML regression{col 57}Number of obs{col 70} = {res} 1,420
{col 57}{txt}Wald chi2({res}9{txt}){col 70} = {res}160.95
{txt}Log likelihood = {res}-2792.1162{col 57}{txt}Prob > chi2{col 70} = {res}0.0000

{txt}{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                        trustel{col 33}{c |} Coefficient{col 45}  Std. err.{col 57}      z{col 65}   P>|z|{col 73}     [95% con{col 86}f. interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}1.indirect {c |}{col 33}{res}{space 2}-.0800758{col 45}{space 2} .1735542{col 56}{space 1}   -0.46{col 65}{space 3}0.645{col 73}{space 4}-.4202358{col 86}{space 3} .2600842
{txt}{space 11}approval_percentiles {c |}{col 33}{res}{space 2} .0069488{col 45}{space 2} .0566449{col 56}{space 1}    0.12{col 65}{space 3}0.902{col 73}{space 4}-.1040731{col 86}{space 3} .1179708
{txt}{space 31} {c |}
indirect#c.approval_percentiles {c |}
{space 29}1  {c |}{col 33}{res}{space 2}-.0504556{col 45}{space 2} .1034126{col 56}{space 1}   -0.49{col 65}{space 3}0.626{col 73}{space 4}-.2531405{col 86}{space 3} .1522294
{txt}{space 31} {c |}
{space 26}woman {c |}{col 33}{res}{space 2}-.0221206{col 45}{space 2} .0924339{col 56}{space 1}   -0.24{col 65}{space 3}0.811{col 73}{space 4}-.2032877{col 86}{space 3} .1590464
{txt}{space 23}quintall {c |}{col 33}{res}{space 2} .1552837{col 45}{space 2} .1431542{col 56}{space 1}    1.08{col 65}{space 3}0.278{col 73}{space 4}-.1252933{col 86}{space 3} .4358608
{txt}{space 22}agecohort {c |}{col 33}{res}{space 2}-.0657071{col 45}{space 2} .1698999{col 56}{space 1}   -0.39{col 65}{space 3}0.699{col 73}{space 4}-.3987047{col 86}{space 3} .2672905
{txt}{space 28}edr {c |}{col 33}{res}{space 2}-.2744392{col 45}{space 2} .2100876{col 56}{space 1}   -1.31{col 65}{space 3}0.191{col 73}{space 4}-.6862033{col 86}{space 3} .1373249
{txt}{space 26}rural {c |}{col 33}{res}{space 2}  .358015{col 45}{space 2} .0982531{col 56}{space 1}    3.64{col 65}{space 3}0.000{col 73}{space 4} .1654424{col 86}{space 3} .5505875
{txt}{space 29}m1 {c |}{col 33}{res}{space 2} 1.988484{col 45}{space 2} .1850311{col 56}{space 1}   10.75{col 65}{space 3}0.000{col 73}{space 4} 1.625829{col 86}{space 3} 2.351138
{txt}{space 26}_cons {c |}{col 33}{res}{space 2} 2.680185{col 45}{space 2} .2087604{col 56}{space 1}   12.84{col 65}{space 3}0.000{col 73}{space 4} 2.271022{col 86}{space 3} 3.089348
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}Std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 2.988229{col 44} .1121462{col 58} 2.776316{col 70} 3.216318
{txt}{hline 29}{c BT}{hline 48}

{com}.         margins, dydx(indirect) at(approval_percentiles=(0 1 2 3))
{res}
{txt}{col 1}Average marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,420}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.indirect}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:3}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.indirect  {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.indirect   {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.0800758{col 26}{space 2} .1735542{col 37}{space 1}   -0.46{col 46}{space 3}0.645{col 54}{space 4}-.4202358{col 67}{space 3} .2600842
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.1305314{col 26}{space 2} .1132992{col 37}{space 1}   -1.15{col 46}{space 3}0.249{col 54}{space 4}-.3525937{col 67}{space 3} .0915309
{txt}{space 10}3  {c |}{col 14}{res}{space 2} -.180987{col 26}{space 2} .1301563{col 37}{space 1}   -1.39{col 46}{space 3}0.164{col 54}{space 4}-.4360887{col 67}{space 3} .0741147
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.2314426{col 26}{space 2} .2059926{col 37}{space 1}   -1.12{col 46}{space 3}0.261{col 54}{space 4}-.6351807{col 67}{space 3} .1722956
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) title("Indirect") ///
>                 ytitle("Effects on Trust in Elections")
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:approval_percentiles}{p_end}
{res}{txt}
{com}.         graph save Graph "a23_indirect_GTM.gph", replace                                
{res}{txt}file {bf:a23_indirect_GTM.gph} saved

{com}. mixed trustel i.direct##c.approval_percentiles woman quintall agecohort edr rural m1 if pais==2
{res}
{txt}Mixed-effects ML regression{col 57}Number of obs{col 70} = {res} 1,427
{col 57}{txt}Wald chi2({res}9{txt}){col 70} = {res}161.64
{txt}Log likelihood = {res}-2807.1641{col 57}{txt}Prob > chi2{col 70} = {res}0.0000

{txt}{hline 30}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                      trustel{col 31}{c |} Coefficient{col 43}  Std. err.{col 55}      z{col 63}   P>|z|{col 71}     [95% con{col 84}f. interval]
{hline 30}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}1.direct {c |}{col 31}{res}{space 2}-.1852542{col 43}{space 2} .2212162{col 54}{space 1}   -0.84{col 63}{space 3}0.402{col 71}{space 4}  -.61883{col 84}{space 3} .2483216
{txt}{space 9}approval_percentiles {c |}{col 31}{res}{space 2}-.0201947{col 43}{space 2} .0521525{col 54}{space 1}   -0.39{col 63}{space 3}0.699{col 71}{space 4}-.1224117{col 84}{space 3} .0820223
{txt}{space 29} {c |}
direct#c.approval_percentiles {c |}
{space 27}1  {c |}{col 31}{res}{space 2} .0582631{col 43}{space 2} .1300445{col 54}{space 1}    0.45{col 63}{space 3}0.654{col 71}{space 4}-.1966194{col 84}{space 3} .3131457
{txt}{space 29} {c |}
{space 24}woman {c |}{col 31}{res}{space 2}-.0314496{col 43}{space 2} .0923021{col 54}{space 1}   -0.34{col 63}{space 3}0.733{col 71}{space 4}-.2123585{col 84}{space 3} .1494592
{txt}{space 21}quintall {c |}{col 31}{res}{space 2} .1519582{col 43}{space 2} .1426584{col 54}{space 1}    1.07{col 63}{space 3}0.287{col 71}{space 4}-.1276472{col 84}{space 3} .4315635
{txt}{space 20}agecohort {c |}{col 31}{res}{space 2}-.0487214{col 43}{space 2}  .168723{col 54}{space 1}   -0.29{col 63}{space 3}0.773{col 71}{space 4}-.3794125{col 84}{space 3} .2819696
{txt}{space 26}edr {c |}{col 31}{res}{space 2}-.3173918{col 43}{space 2} .2081957{col 54}{space 1}   -1.52{col 63}{space 3}0.127{col 71}{space 4}-.7254479{col 84}{space 3} .0906643
{txt}{space 24}rural {c |}{col 31}{res}{space 2} .3566873{col 43}{space 2} .0983512{col 54}{space 1}    3.63{col 63}{space 3}0.000{col 71}{space 4} .1639224{col 84}{space 3} .5494522
{txt}{space 27}m1 {c |}{col 31}{res}{space 2}  2.01379{col 43}{space 2} .1853004{col 54}{space 1}   10.87{col 63}{space 3}0.000{col 71}{space 4} 1.650608{col 84}{space 3} 2.376972
{txt}{space 24}_cons {c |}{col 31}{res}{space 2} 2.712895{col 43}{space 2} .2061341{col 54}{space 1}   13.16{col 63}{space 3}0.000{col 71}{space 4}  2.30888{col 84}{space 3} 3.116911
{txt}{hline 30}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}Std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 2.993611{col 44} .1120723{col 58} 2.781819{col 70} 3.221529
{txt}{hline 29}{c BT}{hline 48}

{com}.         margins, dydx(direct) at(approval_percentiles=(0 1 2 3))
{res}
{txt}{col 1}Average marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,427}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.direct}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:3}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.direct    {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.direct     {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.1852542{col 26}{space 2} .2212162{col 37}{space 1}   -0.84{col 46}{space 3}0.402{col 54}{space 4}  -.61883{col 67}{space 3} .2483216
{txt}{space 10}2  {c |}{col 14}{res}{space 2} -.126991{col 26}{space 2} .1458308{col 37}{space 1}   -0.87{col 46}{space 3}0.384{col 54}{space 4}-.4128142{col 67}{space 3} .1588321
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0687279{col 26}{space 2} .1655892{col 37}{space 1}   -0.42{col 46}{space 3}0.678{col 54}{space 4}-.3932767{col 67}{space 3} .2558209
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0104648{col 26}{space 2} .2596075{col 37}{space 1}   -0.04{col 46}{space 3}0.968{col 54}{space 4}-.5192861{col 67}{space 3} .4983566
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) title("Direct") ///
>                 ytitle("Effects on Trust in Elections")
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:approval_percentiles}{p_end}
{res}{txt}
{com}.         graph save Graph "a23_direct_GTM.gph", replace
{res}{txt}file {bf:a23_direct_GTM.gph} saved

{com}. graph combine a23_direct_GTM.gph a23_indirect_GTM.gph, xsize(8) ysize(4) title("Guatemala")
{res}{txt}
{com}.         graph save Graph "a23_GTM.gph", replace
{res}{txt}file {bf:a23_GTM.gph} saved

{com}.         
.         
. mixed trustel i.indirect##c.approval_percentiles woman quintall agecohort edr rural m1 if pais==11
{res}
{txt}Mixed-effects ML regression{col 57}Number of obs{col 70} = {res} 1,405
{col 57}{txt}Wald chi2({res}9{txt}){col 70} = {res}148.61
{txt}Log likelihood = {res}-2621.9605{col 57}{txt}Prob > chi2{col 70} = {res}0.0000

{txt}{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                        trustel{col 33}{c |} Coefficient{col 45}  Std. err.{col 57}      z{col 65}   P>|z|{col 73}     [95% con{col 86}f. interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}1.indirect {c |}{col 33}{res}{space 2}-.3848438{col 45}{space 2} .1428091{col 56}{space 1}   -2.69{col 65}{space 3}0.007{col 73}{space 4}-.6647444{col 86}{space 3}-.1049432
{txt}{space 11}approval_percentiles {c |}{col 33}{res}{space 2} -.046463{col 45}{space 2} .0520965{col 56}{space 1}   -0.89{col 65}{space 3}0.372{col 73}{space 4}-.1485702{col 86}{space 3} .0556442
{txt}{space 31} {c |}
indirect#c.approval_percentiles {c |}
{space 29}1  {c |}{col 33}{res}{space 2} .1390267{col 45}{space 2}  .095515{col 56}{space 1}    1.46{col 65}{space 3}0.146{col 73}{space 4}-.0481793{col 86}{space 3} .3262327
{txt}{space 31} {c |}
{space 26}woman {c |}{col 33}{res}{space 2}-.4550846{col 45}{space 2} .0845318{col 56}{space 1}   -5.38{col 65}{space 3}0.000{col 73}{space 4} -.620764{col 86}{space 3}-.2894053
{txt}{space 23}quintall {c |}{col 33}{res}{space 2} .1522248{col 45}{space 2} .1282694{col 56}{space 1}    1.19{col 65}{space 3}0.235{col 73}{space 4}-.0991785{col 86}{space 3} .4036282
{txt}{space 22}agecohort {c |}{col 33}{res}{space 2}-.2524232{col 45}{space 2} .1431461{col 56}{space 1}   -1.76{col 65}{space 3}0.078{col 73}{space 4}-.5329843{col 86}{space 3} .0281379
{txt}{space 28}edr {c |}{col 33}{res}{space 2} .1301225{col 45}{space 2} .2084516{col 56}{space 1}    0.62{col 65}{space 3}0.532{col 73}{space 4}-.2784352{col 86}{space 3} .5386803
{txt}{space 26}rural {c |}{col 33}{res}{space 2} .1439251{col 45}{space 2} .1017653{col 56}{space 1}    1.41{col 65}{space 3}0.157{col 73}{space 4}-.0555312{col 86}{space 3} .3433813
{txt}{space 29}m1 {c |}{col 33}{res}{space 2} 2.114159{col 45}{space 2} .2134568{col 56}{space 1}    9.90{col 65}{space 3}0.000{col 73}{space 4} 1.695791{col 86}{space 3} 2.532526
{txt}{space 26}_cons {c |}{col 33}{res}{space 2} 2.663325{col 45}{space 2} .2376611{col 56}{space 1}   11.21{col 65}{space 3}0.000{col 73}{space 4} 2.197518{col 86}{space 3} 3.129133
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}Std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 2.445993{col 44} .0922852{col 58} 2.271643{col 70} 2.633724
{txt}{hline 29}{c BT}{hline 48}

{com}.         margins, dydx(indirect) at(approval_percentiles=(0 1 2 3))
{res}
{txt}{col 1}Average marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,405}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.indirect}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:3}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.indirect  {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.indirect   {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.3848438{col 26}{space 2} .1428091{col 37}{space 1}   -2.69{col 46}{space 3}0.007{col 54}{space 4}-.6647444{col 67}{space 3}-.1049432
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.2458171{col 26}{space 2}  .099144{col 37}{space 1}   -2.48{col 46}{space 3}0.013{col 54}{space 4}-.4401358{col 67}{space 3}-.0514985
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1067904{col 26}{space 2} .1323287{col 37}{space 1}   -0.81{col 46}{space 3}0.420{col 54}{space 4}-.3661499{col 67}{space 3}  .152569
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0322363{col 26}{space 2} .2084189{col 37}{space 1}    0.15{col 46}{space 3}0.877{col 54}{space 4}-.3762574{col 67}{space 3} .4407299
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) title("Indirect") ///
>                 ytitle("Effects on Trust in Elections")
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:approval_percentiles}{p_end}
{res}{txt}
{com}.         graph save Graph "a23_indirect_PER.gph", replace                                
{res}{txt}file {bf:a23_indirect_PER.gph} saved

{com}. mixed trustel i.direct##c.approval_percentiles woman quintall agecohort edr rural m1 if pais==11
{res}
{txt}Mixed-effects ML regression{col 57}Number of obs{col 70} = {res} 1,413
{col 57}{txt}Wald chi2({res}9{txt}){col 70} = {res}146.36
{txt}Log likelihood = {res}-2640.7939{col 57}{txt}Prob > chi2{col 70} = {res}0.0000

{txt}{hline 30}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                      trustel{col 31}{c |} Coefficient{col 43}  Std. err.{col 55}      z{col 63}   P>|z|{col 71}     [95% con{col 84}f. interval]
{hline 30}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}1.direct {c |}{col 31}{res}{space 2}-.3722814{col 43}{space 2} .1930877{col 54}{space 1}   -1.93{col 63}{space 3}0.054{col 71}{space 4}-.7507264{col 84}{space 3} .0061636
{txt}{space 9}approval_percentiles {c |}{col 31}{res}{space 2}-.0105723{col 43}{space 2} .0472466{col 54}{space 1}   -0.22{col 63}{space 3}0.823{col 71}{space 4}-.1031739{col 84}{space 3} .0820292
{txt}{space 29} {c |}
direct#c.approval_percentiles {c |}
{space 27}1  {c |}{col 31}{res}{space 2} .0340534{col 43}{space 2} .1298173{col 54}{space 1}    0.26{col 63}{space 3}0.793{col 71}{space 4}-.2203839{col 84}{space 3} .2884906
{txt}{space 29} {c |}
{space 24}woman {c |}{col 31}{res}{space 2}-.4562855{col 43}{space 2} .0846148{col 54}{space 1}   -5.39{col 63}{space 3}0.000{col 71}{space 4}-.6221274{col 84}{space 3}-.2904437
{txt}{space 21}quintall {c |}{col 31}{res}{space 2} .1255177{col 43}{space 2} .1283938{col 54}{space 1}    0.98{col 63}{space 3}0.328{col 71}{space 4}-.1261295{col 84}{space 3} .3771648
{txt}{space 20}agecohort {c |}{col 31}{res}{space 2}-.2524538{col 43}{space 2} .1424092{col 54}{space 1}   -1.77{col 63}{space 3}0.076{col 71}{space 4}-.5315707{col 84}{space 3} .0266631
{txt}{space 26}edr {c |}{col 31}{res}{space 2} .0992948{col 43}{space 2} .2085937{col 54}{space 1}    0.48{col 63}{space 3}0.634{col 71}{space 4}-.3095414{col 84}{space 3}  .508131
{txt}{space 24}rural {c |}{col 31}{res}{space 2} .1620295{col 43}{space 2} .1018781{col 54}{space 1}    1.59{col 63}{space 3}0.112{col 71}{space 4}-.0376478{col 84}{space 3} .3617069
{txt}{space 27}m1 {c |}{col 31}{res}{space 2} 2.132511{col 43}{space 2} .2135021{col 54}{space 1}    9.99{col 63}{space 3}0.000{col 71}{space 4} 1.714055{col 84}{space 3} 2.550968
{txt}{space 24}_cons {c |}{col 31}{res}{space 2} 2.629347{col 43}{space 2} .2351805{col 54}{space 1}   11.18{col 63}{space 3}0.000{col 71}{space 4} 2.168401{col 84}{space 3} 3.090292
{txt}{hline 30}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}Std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 2.459546{col 44} .0925335{col 58} 2.284709{col 70} 2.647763
{txt}{hline 29}{c BT}{hline 48}

{com}.         margins, dydx(direct) at(approval_percentiles=(0 1 2 3))
{res}
{txt}{col 1}Average marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,413}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.direct}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:3}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.direct    {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.direct     {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.3722814{col 26}{space 2} .1930877{col 37}{space 1}   -1.93{col 46}{space 3}0.054{col 54}{space 4}-.7507264{col 67}{space 3} .0061636
{txt}{space 10}2  {c |}{col 14}{res}{space 2} -.338228{col 26}{space 2} .1361273{col 37}{space 1}   -2.48{col 46}{space 3}0.013{col 54}{space 4}-.6050326{col 67}{space 3}-.0714235
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.3041747{col 26}{space 2} .1829849{col 37}{space 1}   -1.66{col 46}{space 3}0.096{col 54}{space 4}-.6628185{col 67}{space 3} .0544691
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.2701213{col 26}{space 2} .2866032{col 37}{space 1}   -0.94{col 46}{space 3}0.346{col 54}{space 4}-.8318532{col 67}{space 3} .2916105
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) title("Direct") ///
>                 ytitle("Effects on Trust in Elections")
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:approval_percentiles}{p_end}
{res}{txt}
{com}.         graph save Graph "a23_direct_PER.gph", replace
{res}{txt}file {bf:a23_direct_PER.gph} saved

{com}. graph combine a23_direct_PER.gph a23_indirect_PER.gph, xsize(8) ysize(4) title("Peru")
{res}{txt}
{com}.         graph save Graph "a23_PER.gph", replace
{res}{txt}file {bf:a23_PER.gph} saved

{com}.         
.         
. mixed trustel i.indirect##c.approval_percentiles woman quintall agecohort edr rural m1 if pais==12
{res}
{txt}Mixed-effects ML regression{col 57}Number of obs{col 70} = {res} 1,371
{col 57}{txt}Wald chi2({res}9{txt}){col 70} = {res}209.76
{txt}Log likelihood = {res}-2697.5461{col 57}{txt}Prob > chi2{col 70} = {res}0.0000

{txt}{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                        trustel{col 33}{c |} Coefficient{col 45}  Std. err.{col 57}      z{col 65}   P>|z|{col 73}     [95% con{col 86}f. interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}1.indirect {c |}{col 33}{res}{space 2}-.2074488{col 45}{space 2} .1658351{col 56}{space 1}   -1.25{col 65}{space 3}0.211{col 73}{space 4}-.5324796{col 86}{space 3} .1175821
{txt}{space 11}approval_percentiles {c |}{col 33}{res}{space 2} .1437079{col 45}{space 2} .0629752{col 56}{space 1}    2.28{col 65}{space 3}0.022{col 73}{space 4} .0202787{col 86}{space 3} .2671371
{txt}{space 31} {c |}
indirect#c.approval_percentiles {c |}
{space 29}1  {c |}{col 33}{res}{space 2}-.0788875{col 45}{space 2} .1010808{col 56}{space 1}   -0.78{col 65}{space 3}0.435{col 73}{space 4}-.2770022{col 86}{space 3} .1192272
{txt}{space 31} {c |}
{space 26}woman {c |}{col 33}{res}{space 2}-.1825608{col 45}{space 2} .0943779{col 56}{space 1}   -1.93{col 65}{space 3}0.053{col 73}{space 4} -.367538{col 86}{space 3} .0024165
{txt}{space 23}quintall {c |}{col 33}{res}{space 2}-.0207786{col 45}{space 2}  .146445{col 56}{space 1}   -0.14{col 65}{space 3}0.887{col 73}{space 4}-.3078056{col 86}{space 3} .2662483
{txt}{space 22}agecohort {c |}{col 33}{res}{space 2} .0057132{col 45}{space 2} .1631004{col 56}{space 1}    0.04{col 65}{space 3}0.972{col 73}{space 4}-.3139578{col 86}{space 3} .3253841
{txt}{space 28}edr {c |}{col 33}{res}{space 2} .4188136{col 45}{space 2} .2326575{col 56}{space 1}    1.80{col 65}{space 3}0.072{col 73}{space 4}-.0371867{col 86}{space 3}  .874814
{txt}{space 26}rural {c |}{col 33}{res}{space 2} .2566702{col 45}{space 2} .1017676{col 56}{space 1}    2.52{col 65}{space 3}0.012{col 73}{space 4} .0572094{col 86}{space 3}  .456131
{txt}{space 29}m1 {c |}{col 33}{res}{space 2} 2.551107{col 45}{space 2} .1999687{col 56}{space 1}   12.76{col 65}{space 3}0.000{col 73}{space 4} 2.159175{col 86}{space 3} 2.943038
{txt}{space 26}_cons {c |}{col 33}{res}{space 2} 1.773481{col 45}{space 2} .2501015{col 56}{space 1}    7.09{col 65}{space 3}0.000{col 73}{space 4} 1.283291{col 86}{space 3} 2.263671
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}Std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 2.995988{col 44} .1144291{col 58}   2.7799{col 70} 3.228873
{txt}{hline 29}{c BT}{hline 48}

{com}.         margins, dydx(indirect) at(approval_percentiles=(0 1 2 3))
{res}
{txt}{col 1}Average marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,371}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.indirect}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:3}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.indirect  {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.indirect   {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.2074488{col 26}{space 2} .1658351{col 37}{space 1}   -1.25{col 46}{space 3}0.211{col 54}{space 4}-.5324796{col 67}{space 3} .1175821
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.2863362{col 26}{space 2} .1054059{col 37}{space 1}   -2.72{col 46}{space 3}0.007{col 54}{space 4} -.492928{col 67}{space 3}-.0797444
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.3652237{col 26}{space 2} .1231024{col 37}{space 1}   -2.97{col 46}{space 3}0.003{col 54}{space 4}-.6064999{col 67}{space 3}-.1239475
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.4441112{col 26}{space 2} .1990795{col 37}{space 1}   -2.23{col 46}{space 3}0.026{col 54}{space 4}-.8342998{col 67}{space 3}-.0539226
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) title("Indirect") ///
>                 ytitle("Effects on Trust in Elections")
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:approval_percentiles}{p_end}
{res}{txt}
{com}.         graph save Graph "a23_indirect_PRY.gph", replace                                
{res}{txt}file {bf:a23_indirect_PRY.gph} saved

{com}. mixed trustel i.direct##c.approval_percentiles woman quintall agecohort edr rural m1 if pais==12
{res}
{txt}Mixed-effects ML regression{col 57}Number of obs{col 70} = {res} 1,383
{col 57}{txt}Wald chi2({res}9{txt}){col 70} = {res}204.67
{txt}Log likelihood = {res}-2724.3132{col 57}{txt}Prob > chi2{col 70} = {res}0.0000

{txt}{hline 30}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                      trustel{col 31}{c |} Coefficient{col 43}  Std. err.{col 55}      z{col 63}   P>|z|{col 71}     [95% con{col 84}f. interval]
{hline 30}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}1.direct {c |}{col 31}{res}{space 2}-.4290762{col 43}{space 2} .2176357{col 54}{space 1}   -1.97{col 63}{space 3}0.049{col 71}{space 4}-.8556344{col 84}{space 3} -.002518
{txt}{space 9}approval_percentiles {c |}{col 31}{res}{space 2} .1056525{col 43}{space 2}  .055011{col 54}{space 1}    1.92{col 63}{space 3}0.055{col 71}{space 4} -.002167{col 84}{space 3} .2134719
{txt}{space 29} {c |}
direct#c.approval_percentiles {c |}
{space 27}1  {c |}{col 31}{res}{space 2} .0724453{col 43}{space 2} .1242856{col 54}{space 1}    0.58{col 63}{space 3}0.560{col 71}{space 4}  -.17115{col 84}{space 3} .3160407
{txt}{space 29} {c |}
{space 24}woman {c |}{col 31}{res}{space 2}-.1672629{col 43}{space 2} .0941444{col 54}{space 1}   -1.78{col 63}{space 3}0.076{col 71}{space 4}-.3517825{col 84}{space 3} .0172567
{txt}{space 21}quintall {c |}{col 31}{res}{space 2}-.0533642{col 43}{space 2} .1456955{col 54}{space 1}   -0.37{col 63}{space 3}0.714{col 71}{space 4}-.3389221{col 84}{space 3} .2321936
{txt}{space 20}agecohort {c |}{col 31}{res}{space 2}-.0235947{col 43}{space 2} .1613815{col 54}{space 1}   -0.15{col 63}{space 3}0.884{col 71}{space 4}-.3398967{col 84}{space 3} .2927073
{txt}{space 26}edr {c |}{col 31}{res}{space 2} .4338622{col 43}{space 2} .2319533{col 54}{space 1}    1.87{col 63}{space 3}0.061{col 71}{space 4}-.0207579{col 84}{space 3} .8884824
{txt}{space 24}rural {c |}{col 31}{res}{space 2} .2539736{col 43}{space 2} .1014304{col 54}{space 1}    2.50{col 63}{space 3}0.012{col 71}{space 4} .0551736{col 84}{space 3} .4527735
{txt}{space 27}m1 {c |}{col 31}{res}{space 2} 2.546742{col 43}{space 2} .1998368{col 54}{space 1}   12.74{col 63}{space 3}0.000{col 71}{space 4} 2.155069{col 84}{space 3} 2.938415
{txt}{space 24}_cons {c |}{col 31}{res}{space 2} 1.773899{col 43}{space 2} .2430159{col 54}{space 1}    7.30{col 63}{space 3}0.000{col 71}{space 4} 1.297597{col 84}{space 3} 2.250202
{txt}{hline 30}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}Std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 3.009694{col 44} .1144527{col 58} 2.793527{col 70} 3.242589
{txt}{hline 29}{c BT}{hline 48}

{com}.         margins, dydx(direct) at(approval_percentiles=(0 1 2 3))
{res}
{txt}{col 1}Average marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,383}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.direct}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:3}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.direct    {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.direct     {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.4290762{col 26}{space 2} .2176357{col 37}{space 1}   -1.97{col 46}{space 3}0.049{col 54}{space 4}-.8556344{col 67}{space 3} -.002518
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.3566309{col 26}{space 2} .1402463{col 37}{space 1}   -2.54{col 46}{space 3}0.011{col 54}{space 4}-.6315086{col 67}{space 3}-.0817532
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.2841856{col 26}{space 2}  .151217{col 37}{space 1}   -1.88{col 46}{space 3}0.060{col 54}{space 4}-.5805655{col 67}{space 3} .0121943
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.2117403{col 26}{space 2} .2386587{col 37}{space 1}   -0.89{col 46}{space 3}0.375{col 54}{space 4}-.6795027{col 67}{space 3} .2560222
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) title("Direct") ///
>                 ytitle("Effects on Trust in Elections")
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:approval_percentiles}{p_end}
{res}{txt}
{com}.         graph save Graph "a23_direct_PRY.gph", replace
{res}{txt}file {bf:a23_direct_PRY.gph} saved

{com}. graph combine a23_direct_PRY.gph a23_indirect_PRY.gph, xsize(8) ysize(4) title("Paraguay")
{res}{txt}
{com}.         graph save Graph "a23_PRY.gph", replace
{res}{txt}file {bf:a23_PRY.gph} saved

{com}.         
.         
. mixed trustel i.indirect##c.approval_percentiles woman quintall agecohort edr rural m1 if pais==21
{res}
{txt}Mixed-effects ML regression{col 57}Number of obs{col 70} = {res} 1,396
{col 57}{txt}Wald chi2({res}9{txt}){col 70} = {res}315.94
{txt}Log likelihood = {res}-2820.7892{col 57}{txt}Prob > chi2{col 70} = {res}0.0000

{txt}{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                        trustel{col 33}{c |} Coefficient{col 45}  Std. err.{col 57}      z{col 65}   P>|z|{col 73}     [95% con{col 86}f. interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}1.indirect {c |}{col 33}{res}{space 2}-.2191389{col 45}{space 2} .1819028{col 56}{space 1}   -1.20{col 65}{space 3}0.228{col 73}{space 4}-.5756619{col 86}{space 3} .1373842
{txt}{space 11}approval_percentiles {c |}{col 33}{res}{space 2} .0187198{col 45}{space 2} .0613637{col 56}{space 1}    0.31{col 65}{space 3}0.760{col 73}{space 4}-.1015509{col 86}{space 3} .1389905
{txt}{space 31} {c |}
indirect#c.approval_percentiles {c |}
{space 29}1  {c |}{col 33}{res}{space 2} .1391568{col 45}{space 2} .0947878{col 56}{space 1}    1.47{col 65}{space 3}0.142{col 73}{space 4}-.0466239{col 86}{space 3} .3249376
{txt}{space 31} {c |}
{space 26}woman {c |}{col 33}{res}{space 2} .1858741{col 45}{space 2} .1000631{col 56}{space 1}    1.86{col 65}{space 3}0.063{col 73}{space 4}-.0102459{col 86}{space 3} .3819941
{txt}{space 23}quintall {c |}{col 33}{res}{space 2}-.3215955{col 45}{space 2} .1551801{col 56}{space 1}   -2.07{col 65}{space 3}0.038{col 73}{space 4} -.625743{col 86}{space 3}-.0174481
{txt}{space 22}agecohort {c |}{col 33}{res}{space 2} .0731359{col 45}{space 2} .1700615{col 56}{space 1}    0.43{col 65}{space 3}0.667{col 73}{space 4}-.2601785{col 86}{space 3} .4064503
{txt}{space 28}edr {c |}{col 33}{res}{space 2}-.3474508{col 45}{space 2} .2233693{col 56}{space 1}   -1.56{col 65}{space 3}0.120{col 73}{space 4}-.7852466{col 86}{space 3} .0903449
{txt}{space 26}rural {c |}{col 33}{res}{space 2} .0888362{col 45}{space 2} .1137867{col 56}{space 1}    0.78{col 65}{space 3}0.435{col 73}{space 4}-.1341816{col 86}{space 3} .3118539
{txt}{space 29}m1 {c |}{col 33}{res}{space 2} 3.083234{col 45}{space 2} .1918225{col 56}{space 1}   16.07{col 65}{space 3}0.000{col 73}{space 4} 2.707269{col 86}{space 3} 3.459199
{txt}{space 26}_cons {c |}{col 33}{res}{space 2} 1.685247{col 45}{space 2} .2473622{col 56}{space 1}    6.81{col 65}{space 3}0.000{col 73}{space 4} 1.200426{col 86}{space 3} 2.170068
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}Std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 3.331319{col 44} .1260923{col 58} 3.093127{col 70} 3.587853
{txt}{hline 29}{c BT}{hline 48}

{com}.         margins, dydx(indirect) at(approval_percentiles=(0 1 2 3))
{res}
{txt}{col 1}Average marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,396}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.indirect}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:3}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.indirect  {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.indirect   {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.2191389{col 26}{space 2} .1819028{col 37}{space 1}   -1.20{col 46}{space 3}0.228{col 54}{space 4}-.5756619{col 67}{space 3} .1373842
{txt}{space 10}2  {c |}{col 14}{res}{space 2} -.079982{col 26}{space 2} .1174808{col 37}{space 1}   -0.68{col 46}{space 3}0.496{col 54}{space 4}-.3102401{col 67}{space 3}  .150276
{txt}{space 10}3  {c |}{col 14}{res}{space 2} .0591748{col 26}{space 2} .1117331{col 37}{space 1}    0.53{col 46}{space 3}0.596{col 54}{space 4} -.159818{col 67}{space 3} .2781676
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .1983316{col 26}{space 2} .1706936{col 37}{space 1}    1.16{col 46}{space 3}0.245{col 54}{space 4}-.1362217{col 67}{space 3} .5328849
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) title("Indirect") ///
>                 ytitle("Effects on Trust in Elections")
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:approval_percentiles}{p_end}
{res}{txt}
{com}.         graph save Graph "a23_indirect_DOM.gph", replace                                
{res}{txt}file {bf:a23_indirect_DOM.gph} saved

{com}. mixed trustel i.direct##c.approval_percentiles woman quintall agecohort edr rural m1 if pais==21
{res}
{txt}Mixed-effects ML regression{col 57}Number of obs{col 70} = {res} 1,408
{col 57}{txt}Wald chi2({res}9{txt}){col 70} = {res}324.58
{txt}Log likelihood = {res}-2842.4815{col 57}{txt}Prob > chi2{col 70} = {res}0.0000

{txt}{hline 30}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                      trustel{col 31}{c |} Coefficient{col 43}  Std. err.{col 55}      z{col 63}   P>|z|{col 71}     [95% con{col 84}f. interval]
{hline 30}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}1.direct {c |}{col 31}{res}{space 2}-.1633889{col 43}{space 2} .2025933{col 54}{space 1}   -0.81{col 63}{space 3}0.420{col 71}{space 4}-.5604644{col 84}{space 3} .2336866
{txt}{space 9}approval_percentiles {c |}{col 31}{res}{space 2} .1020527{col 43}{space 2} .0555079{col 54}{space 1}    1.84{col 63}{space 3}0.066{col 71}{space 4}-.0067408{col 84}{space 3} .2108461
{txt}{space 29} {c |}
direct#c.approval_percentiles {c |}
{space 27}1  {c |}{col 31}{res}{space 2}-.0521914{col 43}{space 2} .1032026{col 54}{space 1}   -0.51{col 63}{space 3}0.613{col 71}{space 4}-.2544648{col 84}{space 3}  .150082
{txt}{space 29} {c |}
{space 24}woman {c |}{col 31}{res}{space 2} .1572403{col 43}{space 2} .0997206{col 54}{space 1}    1.58{col 63}{space 3}0.115{col 71}{space 4}-.0382085{col 84}{space 3} .3526892
{txt}{space 21}quintall {c |}{col 31}{res}{space 2}-.3119852{col 43}{space 2} .1541922{col 54}{space 1}   -2.02{col 63}{space 3}0.043{col 71}{space 4}-.6141963{col 84}{space 3} -.009774
{txt}{space 20}agecohort {c |}{col 31}{res}{space 2} .0425976{col 43}{space 2} .1679776{col 54}{space 1}    0.25{col 63}{space 3}0.800{col 71}{space 4}-.2866325{col 84}{space 3} .3718276
{txt}{space 26}edr {c |}{col 31}{res}{space 2}-.3763737{col 43}{space 2} .2218648{col 54}{space 1}   -1.70{col 63}{space 3}0.090{col 71}{space 4}-.8112208{col 84}{space 3} .0584734
{txt}{space 24}rural {c |}{col 31}{res}{space 2} .0875019{col 43}{space 2} .1131814{col 54}{space 1}    0.77{col 63}{space 3}0.439{col 71}{space 4}-.1343295{col 84}{space 3} .3093333
{txt}{space 27}m1 {c |}{col 31}{res}{space 2} 3.032862{col 43}{space 2} .1912714{col 54}{space 1}   15.86{col 63}{space 3}0.000{col 71}{space 4} 2.657977{col 84}{space 3} 3.407747
{txt}{space 24}_cons {c |}{col 31}{res}{space 2} 1.681504{col 43}{space 2} .2426374{col 54}{space 1}    6.93{col 63}{space 3}0.000{col 71}{space 4} 1.205944{col 84}{space 3} 2.157065
{txt}{hline 30}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}Std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33}  3.31925{col 44} .1250989{col 58} 3.082897{col 70} 3.573722
{txt}{hline 29}{c BT}{hline 48}

{com}.         margins, dydx(direct) at(approval_percentiles=(0 1 2 3))
{res}
{txt}{col 1}Average marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,408}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.direct}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:3}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.direct    {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.direct     {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.1633889{col 26}{space 2} .2025933{col 37}{space 1}   -0.81{col 46}{space 3}0.420{col 54}{space 4}-.5604644{col 67}{space 3} .2336866
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.2155803{col 26}{space 2} .1318703{col 37}{space 1}   -1.63{col 46}{space 3}0.102{col 54}{space 4}-.4740413{col 67}{space 3} .0428807
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.2677717{col 26}{space 2} .1226258{col 37}{space 1}   -2.18{col 46}{space 3}0.029{col 54}{space 4}-.5081138{col 67}{space 3}-.0274296
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.3199631{col 26}{space 2} .1843528{col 37}{space 1}   -1.74{col 46}{space 3}0.083{col 54}{space 4}-.6812879{col 67}{space 3} .0413617
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) title("Direct") ///
>                 ytitle("Effects on Trust in Elections")
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:approval_percentiles}{p_end}
{res}{txt}
{com}.         graph save Graph "a23_direct_DOM.gph", replace
{res}{txt}file {bf:a23_direct_DOM.gph} saved

{com}. graph combine a23_direct_DOM.gph a23_indirect_DOM.gph, xsize(8) ysize(4) title("Dominican Republic")
{res}{txt}
{com}.         graph save Graph "a23_DOM.gph", replace
{res}{txt}file {bf:a23_DOM.gph} saved

{com}.         
.         
. mixed trustel i.indirect##c.approval_percentiles woman quintall agecohort edr rural m1 if pais==23
{res}
{txt}Mixed-effects ML regression{col 57}Number of obs{col 70} = {res} 1,224
{col 57}{txt}Wald chi2({res}9{txt}){col 70} = {res}115.98
{txt}Log likelihood = {res}-2546.0263{col 57}{txt}Prob > chi2{col 70} = {res}0.0000

{txt}{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                        trustel{col 33}{c |} Coefficient{col 45}  Std. err.{col 57}      z{col 65}   P>|z|{col 73}     [95% con{col 86}f. interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}1.indirect {c |}{col 33}{res}{space 2} .2359406{col 45}{space 2} .2215898{col 56}{space 1}    1.06{col 65}{space 3}0.287{col 73}{space 4}-.1983675{col 86}{space 3} .6702487
{txt}{space 11}approval_percentiles {c |}{col 33}{res}{space 2} .0842349{col 45}{space 2}  .066179{col 56}{space 1}    1.27{col 65}{space 3}0.203{col 73}{space 4}-.0454736{col 86}{space 3} .2139435
{txt}{space 31} {c |}
indirect#c.approval_percentiles {c |}
{space 29}1  {c |}{col 33}{res}{space 2} -.246926{col 45}{space 2} .1163233{col 56}{space 1}   -2.12{col 65}{space 3}0.034{col 73}{space 4}-.4749154{col 86}{space 3}-.0189365
{txt}{space 31} {c |}
{space 26}woman {c |}{col 33}{res}{space 2}-.0592081{col 45}{space 2} .1116082{col 56}{space 1}   -0.53{col 65}{space 3}0.596{col 73}{space 4}-.2779562{col 86}{space 3} .1595399
{txt}{space 23}quintall {c |}{col 33}{res}{space 2} .0596286{col 45}{space 2} .1631453{col 56}{space 1}    0.37{col 65}{space 3}0.715{col 73}{space 4}-.2601303{col 86}{space 3} .3793874
{txt}{space 22}agecohort {c |}{col 33}{res}{space 2}  1.03241{col 45}{space 2} .1975804{col 56}{space 1}    5.23{col 65}{space 3}0.000{col 73}{space 4} .6451599{col 86}{space 3} 1.419661
{txt}{space 28}edr {c |}{col 33}{res}{space 2}-.2293295{col 45}{space 2} .3071293{col 56}{space 1}   -0.75{col 65}{space 3}0.455{col 73}{space 4}-.8312919{col 86}{space 3} .3726329
{txt}{space 26}rural {c |}{col 33}{res}{space 2}-.0398078{col 45}{space 2} .1140206{col 56}{space 1}   -0.35{col 65}{space 3}0.727{col 73}{space 4}-.2632841{col 86}{space 3} .1836684
{txt}{space 29}m1 {c |}{col 33}{res}{space 2}  1.74113{col 45}{space 2} .2155624{col 56}{space 1}    8.08{col 65}{space 3}0.000{col 73}{space 4} 1.318635{col 86}{space 3} 2.163624
{txt}{space 26}_cons {c |}{col 33}{res}{space 2}  1.93015{col 45}{space 2} .3276742{col 56}{space 1}    5.89{col 65}{space 3}0.000{col 73}{space 4}  1.28792{col 86}{space 3} 2.572379
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}Std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 3.752028{col 44} .1516668{col 58} 3.466237{col 70} 4.061382
{txt}{hline 29}{c BT}{hline 48}

{com}.         margins, dydx(indirect) at(approval_percentiles=(0 1 2 3))
{res}
{txt}{col 1}Average marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,224}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.indirect}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:3}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.indirect  {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.indirect   {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .2359406{col 26}{space 2} .2215898{col 37}{space 1}    1.06{col 46}{space 3}0.287{col 54}{space 4}-.1983675{col 67}{space 3} .6702487
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.0109853{col 26}{space 2} .1458652{col 37}{space 1}   -0.08{col 46}{space 3}0.940{col 54}{space 4}-.2968758{col 67}{space 3} .2749051
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.2579113{col 26}{space 2} .1432252{col 37}{space 1}   -1.80{col 46}{space 3}0.072{col 54}{space 4}-.5386276{col 67}{space 3}  .022805
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.5048373{col 26}{space 2} .2163619{col 37}{space 1}   -2.33{col 46}{space 3}0.020{col 54}{space 4}-.9288989{col 67}{space 3}-.0807757
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) title("Indirect") ///
>                 ytitle("Effects on Trust in Elections")
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:approval_percentiles}{p_end}
{res}{txt}
{com}.         graph save Graph "a23_indirect_JAM.gph", replace                                
{res}{txt}file {bf:a23_indirect_JAM.gph} saved

{com}. mixed trustel i.direct##c.approval_percentiles woman quintall agecohort edr rural m1 if pais==23
{res}
{txt}Mixed-effects ML regression{col 57}Number of obs{col 70} = {res} 1,258
{col 57}{txt}Wald chi2({res}9{txt}){col 70} = {res}111.82
{txt}Log likelihood = {res}-2618.7533{col 57}{txt}Prob > chi2{col 70} = {res}0.0000

{txt}{hline 30}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                      trustel{col 31}{c |} Coefficient{col 43}  Std. err.{col 55}      z{col 63}   P>|z|{col 71}     [95% con{col 84}f. interval]
{hline 30}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}1.direct {c |}{col 31}{res}{space 2}-.1562506{col 43}{space 2} .3720522{col 54}{space 1}   -0.42{col 63}{space 3}0.675{col 71}{space 4}-.8854595{col 84}{space 3} .5729583
{txt}{space 9}approval_percentiles {c |}{col 31}{res}{space 2} .0324319{col 43}{space 2} .0571524{col 54}{space 1}    0.57{col 63}{space 3}0.570{col 71}{space 4}-.0795849{col 84}{space 3} .1444486
{txt}{space 29} {c |}
direct#c.approval_percentiles {c |}
{space 27}1  {c |}{col 31}{res}{space 2}-.0469836{col 43}{space 2} .1763338{col 54}{space 1}   -0.27{col 63}{space 3}0.790{col 71}{space 4}-.3925915{col 84}{space 3} .2986244
{txt}{space 29} {c |}
{space 24}woman {c |}{col 31}{res}{space 2}-.0433476{col 43}{space 2} .1102368{col 54}{space 1}   -0.39{col 63}{space 3}0.694{col 71}{space 4}-.2594078{col 84}{space 3} .1727126
{txt}{space 21}quintall {c |}{col 31}{res}{space 2} .0326424{col 43}{space 2} .1616455{col 54}{space 1}    0.20{col 63}{space 3}0.840{col 71}{space 4} -.284177{col 84}{space 3} .3494618
{txt}{space 20}agecohort {c |}{col 31}{res}{space 2}   1.0082{col 43}{space 2} .1923477{col 54}{space 1}    5.24{col 63}{space 3}0.000{col 71}{space 4} .6312056{col 84}{space 3} 1.385195
{txt}{space 26}edr {c |}{col 31}{res}{space 2}-.1588566{col 43}{space 2} .3040536{col 54}{space 1}   -0.52{col 63}{space 3}0.601{col 71}{space 4}-.7547907{col 84}{space 3} .4370776
{txt}{space 24}rural {c |}{col 31}{res}{space 2}-.0257626{col 43}{space 2} .1128924{col 54}{space 1}   -0.23{col 63}{space 3}0.819{col 71}{space 4}-.2470277{col 84}{space 3} .1955025
{txt}{space 27}m1 {c |}{col 31}{res}{space 2} 1.754045{col 43}{space 2} .2135389{col 54}{space 1}    8.21{col 63}{space 3}0.000{col 71}{space 4} 1.335516{col 84}{space 3} 2.172573
{txt}{space 24}_cons {c |}{col 31}{res}{space 2} 1.926246{col 43}{space 2} .3186504{col 54}{space 1}    6.05{col 63}{space 3}0.000{col 71}{space 4} 1.301703{col 84}{space 3} 2.550789
{txt}{hline 30}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}{hline 29}{c TT}{hline 48}
{col 3}Random-effects parameters{col 30}{c |}{col 34}Estimate{col 45}Std. err.{col 59}[95% conf. interval]
{hline 29}{c +}{hline 48}
{col 16}var(Residual){col 30}{c |}{res}{col 33} 3.764002{col 44} .1500806{col 58}  3.48105{col 70} 4.069954
{txt}{hline 29}{c BT}{hline 48}

{com}.         margins, dydx(direct) at(approval_percentiles=(0 1 2 3))
{res}
{txt}{col 1}Average marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,258}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, fixed portion, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.direct}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 16:approval_perce~s} = {res:{ralign 1:3}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.direct    {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.direct     {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.1562506{col 26}{space 2} .3720522{col 37}{space 1}   -0.42{col 46}{space 3}0.675{col 54}{space 4}-.8854595{col 67}{space 3} .5729583
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.2032342{col 26}{space 2} .2373627{col 37}{space 1}   -0.86{col 46}{space 3}0.392{col 54}{space 4}-.6684566{col 67}{space 3} .2619882
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.2502178{col 26}{space 2} .1909097{col 37}{space 1}   -1.31{col 46}{space 3}0.190{col 54}{space 4}-.6243939{col 67}{space 3} .1239584
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.2972013{col 26}{space 2} .2806051{col 37}{space 1}   -1.06{col 46}{space 3}0.290{col 54}{space 4}-.8471772{col 67}{space 3} .2527745
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) title("Direct") ///
>                 ytitle("Effects on Trust in Elections")
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:approval_percentiles}{p_end}
{res}{txt}
{com}.         graph save Graph "a23_direct_JAM.gph", replace
{res}{txt}file {bf:a23_direct_JAM.gph} saved

{com}. graph combine a23_direct_JAM.gph a23_indirect_JAM.gph, xsize(8) ysize(4) title("Jamaica")
{res}{txt}
{com}.         graph save Graph "a23_JAM.gph", replace
{res}{txt}file {bf:a23_JAM.gph} saved

{com}.         
.         
.         
. ********************************************************************* Appendix analyses for experiment 
. cd "../data"
{res}C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\data
{txt}
{com}. 
. * delete rows 2-14 of qualtrics data set, save as "mex_experiment_cps.csv" and import into stata
. import delimited mex_experiment_cps.csv, clear
{res}{txt}(encoding automatically selected: UTF-8)
{text}(20 vars, 1,779 obs)

{com}. destring, replace
{txt}durationinseconds already numeric; no {res}replace
{txt}responseid: contains nonnumeric characters; no {res}replace
{txt}q2 already numeric; no {res}replace
{txt}birth already numeric; no {res}replace
{txt}citizenship already numeric; no {res}replace
{txt}gender already numeric; no {res}replace
{txt}b47b already numeric; no {res}replace
{txt}norms1 already numeric; no {res}replace
{txt}norms2 already numeric; no {res}replace
{txt}q78_firstclick already numeric; no {res}replace
{txt}exp1a already numeric; no {res}replace
{txt}exp1b already numeric; no {res}replace
{txt}exp1c already numeric; no {res}replace
{txt}b47a already numeric; no {res}replace
{txt}b13 already numeric; no {res}replace
{txt}b20 already numeric; no {res}replace
{txt}b81 already numeric; no {res}replace
{txt}ed already numeric; no {res}replace
{txt}etid already numeric; no {res}replace
{txt}religid already numeric; no {res}replace
{txt}
{com}. 
. * keeping only valid observations 
. drop if q2!=1 // 33 dropped because no consent
{txt}(33 observations deleted)

{com}. drop if citizenship!=1 // 35 dropped because not Mexican citizens
{txt}(35 observations deleted)

{com}. drop if birth==1 // 2 dropped because not 18/older
{txt}(2 observations deleted)

{com}. 
. * age
. replace birth=birth+16
{txt}(1,709 real changes made)

{com}.         sum birth // 18-66

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 7}birth {c |}{res}      1,709    36.60913    14.29881         18         66
{txt}
{com}. gen age=.
{txt}(1,709 missing values generated)

{com}.         replace age=6 if birth>65
{txt}(88 real changes made)

{com}.         replace age=5 if birth<66
{txt}(1,621 real changes made)

{com}.         replace age=4 if birth<56
{txt}(1,470 real changes made)

{com}.         replace age=3 if birth<46
{txt}(1,253 real changes made)

{com}.         replace age=2 if birth<36 
{txt}(918 real changes made)

{com}.         replace age=1 if birth<26
{txt}(521 real changes made)

{com}. lab define age 1"18-25" 2"26-35" 3"36-45" 4"46-55" 5"56-65" 6"66+"
{txt}
{com}. lab val age age
{txt}
{com}. 
. * gender
. recode gender(1=0)(2=1)(3=.), gen(woman)
{txt}(1,709 differences between {bf:gender} and {bf:woman})

{com}. 
. * education
. lab var ed "Education Level"
{txt}
{com}. lab def ed 1"None" 2"Primary" 3"Secondary" 4"University or above"
{txt}
{com}. lab val ed ed
{txt}
{com}. 
. * norms
. recode norms1 (1=5)(2=4)(4=2)(5=1)
{txt}(1,262 changes made to {bf:norms1})

{com}. recode norms2 (1=5)(2=4)(4=2)(5=1)
{txt}(1,137 changes made to {bf:norms2})

{com}. gen norm=(norms1+norms2)/2
{txt}(47 missing values generated)

{com}. rescale norm 0 1
{txt}(1,662 real changes made)

{com}. 
. * DVs
. lab var b47a "Trust in elections"
{txt}
{com}.         tab b47a

   {txt}Trust in {c |}
  elections {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          2 {c |}{res}        392       24.65       24.65
{txt}          3 {c |}{res}        245       15.41       40.06
{txt}          4 {c |}{res}        336       21.13       61.19
{txt}          5 {c |}{res}        271       17.04       78.24
{txt}          6 {c |}{res}         97        6.10       84.34
{txt}          7 {c |}{res}         53        3.33       87.67
{txt}          8 {c |}{res}        196       12.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,590      100.00
{txt}
{com}.         replace b47a=b47a-1
{txt}(1,590 real changes made)

{com}. lab var b13 "Trust in the Congress"
{txt}
{com}. lab var b20 "Trust in the Catholic Church"
{txt}
{com}. lab var b81 "Trust in the health ministry"
{txt}
{com}. 
. * saturated
. lab var exp1c "Seen similar story before"
{txt}
{com}. recode exp1c(2=0)
{txt}(454 changes made to {bf:exp1c})

{com}. 
. *speeders
. sum durationinseconds if etid!=. | religid!=., detail //Among those who completed the survey, nearly 9 in 10 respondents finished before 2283 seconds. 

                    {txt}Duration (in seconds)
{hline 61}
      Percentiles      Smallest
 1%    {res}      317            212
{txt} 5%    {res}      495            214
{txt}10%    {res}      585            238       {txt}Obs         {res}      1,525
{txt}25%    {res}      765            250       {txt}Sum of wgt. {res}      1,525

{txt}50%    {res}     1018                      {txt}Mean          {res} 4368.387
                        {txt}Largest       Std. dev.     {res} 26944.05
{txt}75%    {res}     1501         267508
{txt}90%    {res}     2666         431827       {txt}Variance      {res} 7.26e+08
{txt}95%    {res}     4945         439873       {txt}Skewness      {res} 12.51888
{txt}99%    {res}   134644         508750       {txt}Kurtosis      {res} 188.2894
{txt}
{com}.         tab durationinseconds if etid!=. | religid!=. // fastest 2.43% categorized as speeders using this cutoff

   {txt}Duration {c |}
        (in {c |}
   seconds) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
        212 {c |}{res}          1        0.07        0.07
{txt}        214 {c |}{res}          1        0.07        0.13
{txt}        238 {c |}{res}          1        0.07        0.20
{txt}        250 {c |}{res}          1        0.07        0.26
{txt}        255 {c |}{res}          1        0.07        0.33
{txt}        256 {c |}{res}          1        0.07        0.39
{txt}        267 {c |}{res}          2        0.13        0.52
{txt}        276 {c |}{res}          1        0.07        0.59
{txt}        283 {c |}{res}          1        0.07        0.66
{txt}        284 {c |}{res}          1        0.07        0.72
{txt}        294 {c |}{res}          1        0.07        0.79
{txt}        297 {c |}{res}          1        0.07        0.85
{txt}        306 {c |}{res}          1        0.07        0.92
{txt}        314 {c |}{res}          1        0.07        0.98
{txt}        317 {c |}{res}          1        0.07        1.05
{txt}        335 {c |}{res}          1        0.07        1.11
{txt}        336 {c |}{res}          2        0.13        1.25
{txt}        355 {c |}{res}          1        0.07        1.31
{txt}        366 {c |}{res}          1        0.07        1.38
{txt}        368 {c |}{res}          1        0.07        1.44
{txt}        376 {c |}{res}          1        0.07        1.51
{txt}        384 {c |}{res}          1        0.07        1.57
{txt}        385 {c |}{res}          1        0.07        1.64
{txt}        388 {c |}{res}          1        0.07        1.70
{txt}        393 {c |}{res}          2        0.13        1.84
{txt}        396 {c |}{res}          1        0.07        1.90
{txt}        397 {c |}{res}          1        0.07        1.97
{txt}        400 {c |}{res}          1        0.07        2.03
{txt}        406 {c |}{res}          1        0.07        2.10
{txt}        411 {c |}{res}          1        0.07        2.16
{txt}        419 {c |}{res}          2        0.13        2.30
{txt}        423 {c |}{res}          1        0.07        2.36
{txt}        427 {c |}{res}          1        0.07        2.43
{txt}        428 {c |}{res}          1        0.07        2.49
{txt}        435 {c |}{res}          1        0.07        2.56
{txt}        439 {c |}{res}          1        0.07        2.62
{txt}        440 {c |}{res}          1        0.07        2.69
{txt}        442 {c |}{res}          1        0.07        2.75
{txt}        446 {c |}{res}          1        0.07        2.82
{txt}        448 {c |}{res}          2        0.13        2.95
{txt}        451 {c |}{res}          1        0.07        3.02
{txt}        453 {c |}{res}          1        0.07        3.08
{txt}        454 {c |}{res}          1        0.07        3.15
{txt}        455 {c |}{res}          1        0.07        3.21
{txt}        456 {c |}{res}          2        0.13        3.34
{txt}        457 {c |}{res}          1        0.07        3.41
{txt}        458 {c |}{res}          1        0.07        3.48
{txt}        459 {c |}{res}          1        0.07        3.54
{txt}        462 {c |}{res}          1        0.07        3.61
{txt}        463 {c |}{res}          1        0.07        3.67
{txt}        465 {c |}{res}          1        0.07        3.74
{txt}        468 {c |}{res}          2        0.13        3.87
{txt}        469 {c |}{res}          1        0.07        3.93
{txt}        472 {c |}{res}          1        0.07        4.00
{txt}        476 {c |}{res}          1        0.07        4.07
{txt}        477 {c |}{res}          1        0.07        4.13
{txt}        479 {c |}{res}          3        0.20        4.33
{txt}        483 {c |}{res}          1        0.07        4.39
{txt}        484 {c |}{res}          1        0.07        4.46
{txt}        485 {c |}{res}          1        0.07        4.52
{txt}        488 {c |}{res}          2        0.13        4.66
{txt}        489 {c |}{res}          1        0.07        4.72
{txt}        490 {c |}{res}          1        0.07        4.79
{txt}        492 {c |}{res}          1        0.07        4.85
{txt}        494 {c |}{res}          2        0.13        4.98
{txt}        495 {c |}{res}          2        0.13        5.11
{txt}        498 {c |}{res}          2        0.13        5.25
{txt}        500 {c |}{res}          2        0.13        5.38
{txt}        505 {c |}{res}          1        0.07        5.44
{txt}        508 {c |}{res}          2        0.13        5.57
{txt}        510 {c |}{res}          1        0.07        5.64
{txt}        512 {c |}{res}          1        0.07        5.70
{txt}        514 {c |}{res}          1        0.07        5.77
{txt}        518 {c |}{res}          1        0.07        5.84
{txt}        522 {c |}{res}          1        0.07        5.90
{txt}        526 {c |}{res}          1        0.07        5.97
{txt}        527 {c |}{res}          2        0.13        6.10
{txt}        528 {c |}{res}          4        0.26        6.36
{txt}        529 {c |}{res}          1        0.07        6.43
{txt}        532 {c |}{res}          1        0.07        6.49
{txt}        534 {c |}{res}          2        0.13        6.62
{txt}        535 {c |}{res}          2        0.13        6.75
{txt}        536 {c |}{res}          1        0.07        6.82
{txt}        537 {c |}{res}          2        0.13        6.95
{txt}        538 {c |}{res}          1        0.07        7.02
{txt}        539 {c |}{res}          1        0.07        7.08
{txt}        540 {c |}{res}          1        0.07        7.15
{txt}        542 {c |}{res}          2        0.13        7.28
{txt}        543 {c |}{res}          3        0.20        7.48
{txt}        544 {c |}{res}          1        0.07        7.54
{txt}        546 {c |}{res}          1        0.07        7.61
{txt}        547 {c |}{res}          1        0.07        7.67
{txt}        548 {c |}{res}          2        0.13        7.80
{txt}        549 {c |}{res}          1        0.07        7.87
{txt}        550 {c |}{res}          1        0.07        7.93
{txt}        556 {c |}{res}          1        0.07        8.00
{txt}        557 {c |}{res}          1        0.07        8.07
{txt}        560 {c |}{res}          2        0.13        8.20
{txt}        564 {c |}{res}          1        0.07        8.26
{txt}        566 {c |}{res}          2        0.13        8.39
{txt}        567 {c |}{res}          2        0.13        8.52
{txt}        569 {c |}{res}          1        0.07        8.59
{txt}        570 {c |}{res}          2        0.13        8.72
{txt}        571 {c |}{res}          3        0.20        8.92
{txt}        572 {c |}{res}          2        0.13        9.05
{txt}        574 {c |}{res}          3        0.20        9.25
{txt}        577 {c |}{res}          3        0.20        9.44
{txt}        578 {c |}{res}          1        0.07        9.51
{txt}        579 {c |}{res}          2        0.13        9.64
{txt}        582 {c |}{res}          2        0.13        9.77
{txt}        583 {c |}{res}          1        0.07        9.84
{txt}        584 {c |}{res}          1        0.07        9.90
{txt}        585 {c |}{res}          2        0.13       10.03
{txt}        586 {c |}{res}          1        0.07       10.10
{txt}        587 {c |}{res}          1        0.07       10.16
{txt}        589 {c |}{res}          1        0.07       10.23
{txt}        590 {c |}{res}          1        0.07       10.30
{txt}        591 {c |}{res}          1        0.07       10.36
{txt}        592 {c |}{res}          3        0.20       10.56
{txt}        596 {c |}{res}          1        0.07       10.62
{txt}        597 {c |}{res}          1        0.07       10.69
{txt}        598 {c |}{res}          1        0.07       10.75
{txt}        599 {c |}{res}          2        0.13       10.89
{txt}        600 {c |}{res}          1        0.07       10.95
{txt}        602 {c |}{res}          2        0.13       11.08
{txt}        605 {c |}{res}          2        0.13       11.21
{txt}        606 {c |}{res}          1        0.07       11.28
{txt}        607 {c |}{res}          1        0.07       11.34
{txt}        608 {c |}{res}          2        0.13       11.48
{txt}        609 {c |}{res}          2        0.13       11.61
{txt}        610 {c |}{res}          2        0.13       11.74
{txt}        613 {c |}{res}          1        0.07       11.80
{txt}        614 {c |}{res}          3        0.20       12.00
{txt}        615 {c |}{res}          1        0.07       12.07
{txt}        616 {c |}{res}          1        0.07       12.13
{txt}        617 {c |}{res}          1        0.07       12.20
{txt}        619 {c |}{res}          3        0.20       12.39
{txt}        622 {c |}{res}          1        0.07       12.46
{txt}        624 {c |}{res}          2        0.13       12.59
{txt}        625 {c |}{res}          1        0.07       12.66
{txt}        626 {c |}{res}          1        0.07       12.72
{txt}        627 {c |}{res}          1        0.07       12.79
{txt}        628 {c |}{res}          1        0.07       12.85
{txt}        630 {c |}{res}          1        0.07       12.92
{txt}        631 {c |}{res}          1        0.07       12.98
{txt}        632 {c |}{res}          1        0.07       13.05
{txt}        633 {c |}{res}          2        0.13       13.18
{txt}        634 {c |}{res}          3        0.20       13.38
{txt}        635 {c |}{res}          3        0.20       13.57
{txt}        638 {c |}{res}          1        0.07       13.64
{txt}        639 {c |}{res}          1        0.07       13.70
{txt}        641 {c |}{res}          2        0.13       13.84
{txt}        642 {c |}{res}          2        0.13       13.97
{txt}        643 {c |}{res}          1        0.07       14.03
{txt}        644 {c |}{res}          2        0.13       14.16
{txt}        645 {c |}{res}          2        0.13       14.30
{txt}        646 {c |}{res}          1        0.07       14.36
{txt}        647 {c |}{res}          3        0.20       14.56
{txt}        648 {c |}{res}          2        0.13       14.69
{txt}        649 {c |}{res}          2        0.13       14.82
{txt}        652 {c |}{res}          1        0.07       14.89
{txt}        654 {c |}{res}          1        0.07       14.95
{txt}        655 {c |}{res}          3        0.20       15.15
{txt}        656 {c |}{res}          3        0.20       15.34
{txt}        657 {c |}{res}          1        0.07       15.41
{txt}        658 {c |}{res}          2        0.13       15.54
{txt}        659 {c |}{res}          1        0.07       15.61
{txt}        660 {c |}{res}          2        0.13       15.74
{txt}        661 {c |}{res}          2        0.13       15.87
{txt}        662 {c |}{res}          1        0.07       15.93
{txt}        665 {c |}{res}          2        0.13       16.07
{txt}        667 {c |}{res}          1        0.07       16.13
{txt}        668 {c |}{res}          1        0.07       16.20
{txt}        669 {c |}{res}          1        0.07       16.26
{txt}        670 {c |}{res}          3        0.20       16.46
{txt}        671 {c |}{res}          1        0.07       16.52
{txt}        672 {c |}{res}          2        0.13       16.66
{txt}        673 {c |}{res}          2        0.13       16.79
{txt}        674 {c |}{res}          1        0.07       16.85
{txt}        675 {c |}{res}          1        0.07       16.92
{txt}        677 {c |}{res}          2        0.13       17.05
{txt}        678 {c |}{res}          2        0.13       17.18
{txt}        679 {c |}{res}          1        0.07       17.25
{txt}        680 {c |}{res}          2        0.13       17.38
{txt}        681 {c |}{res}          1        0.07       17.44
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{txt}       2320 {c |}{res}          1        0.07       88.13
{txt}       2322 {c |}{res}          1        0.07       88.20
{txt}       2323 {c |}{res}          1        0.07       88.26
{txt}       2342 {c |}{res}          1        0.07       88.33
{txt}       2346 {c |}{res}          1        0.07       88.39
{txt}       2363 {c |}{res}          1        0.07       88.46
{txt}       2386 {c |}{res}          1        0.07       88.52
{txt}       2397 {c |}{res}          1        0.07       88.59
{txt}       2402 {c |}{res}          1        0.07       88.66
{txt}       2410 {c |}{res}          1        0.07       88.72
{txt}       2411 {c |}{res}          1        0.07       88.79
{txt}       2412 {c |}{res}          1        0.07       88.85
{txt}       2413 {c |}{res}          1        0.07       88.92
{txt}       2434 {c |}{res}          1        0.07       88.98
{txt}       2448 {c |}{res}          1        0.07       89.05
{txt}       2461 {c |}{res}          1        0.07       89.11
{txt}       2483 {c |}{res}          2        0.13       89.25
{txt}       2519 {c |}{res}          1        0.07       89.31
{txt}       2528 {c |}{res}          1        0.07       89.38
{txt}       2579 {c |}{res}          1        0.07       89.44
{txt}       2599 {c |}{res}          1        0.07       89.51
{txt}       2603 {c |}{res}          1        0.07       89.57
{txt}       2618 {c |}{res}          1        0.07       89.64
{txt}       2633 {c |}{res}          1        0.07       89.70
{txt}       2640 {c |}{res}          1        0.07       89.77
{txt}       2643 {c |}{res}          2        0.13       89.90
{txt}       2653 {c |}{res}          1        0.07       89.97
{txt}       2666 {c |}{res}          1        0.07       90.03
{txt}       2667 {c |}{res}          1        0.07       90.10
{txt}       2674 {c |}{res}          3        0.20       90.30
{txt}       2682 {c |}{res}          1        0.07       90.36
{txt}       2683 {c |}{res}          1        0.07       90.43
{txt}       2684 {c |}{res}          1        0.07       90.49
{txt}       2716 {c |}{res}          1        0.07       90.56
{txt}       2719 {c |}{res}          1        0.07       90.62
{txt}       2761 {c |}{res}          1        0.07       90.69
{txt}       2768 {c |}{res}          1        0.07       90.75
{txt}       2772 {c |}{res}          1        0.07       90.82
{txt}       2773 {c |}{res}          1        0.07       90.89
{txt}       2774 {c |}{res}          1        0.07       90.95
{txt}       2788 {c |}{res}          1        0.07       91.02
{txt}       2818 {c |}{res}          1        0.07       91.08
{txt}       2820 {c |}{res}          1        0.07       91.15
{txt}       2845 {c |}{res}          1        0.07       91.21
{txt}       2850 {c |}{res}          1        0.07       91.28
{txt}       2862 {c |}{res}          1        0.07       91.34
{txt}       2890 {c |}{res}          1        0.07       91.41
{txt}       2901 {c |}{res}          1        0.07       91.48
{txt}       2909 {c |}{res}          1        0.07       91.54
{txt}       2938 {c |}{res}          1        0.07       91.61
{txt}       2946 {c |}{res}          1        0.07       91.67
{txt}       3005 {c |}{res}          1        0.07       91.74
{txt}       3044 {c |}{res}          1        0.07       91.80
{txt}       3046 {c |}{res}          1        0.07       91.87
{txt}       3072 {c |}{res}          1        0.07       91.93
{txt}       3075 {c |}{res}          1        0.07       92.00
{txt}       3076 {c |}{res}          1        0.07       92.07
{txt}       3079 {c |}{res}          1        0.07       92.13
{txt}       3118 {c |}{res}          1        0.07       92.20
{txt}       3120 {c |}{res}          1        0.07       92.26
{txt}       3121 {c |}{res}          1        0.07       92.33
{txt}       3132 {c |}{res}          1        0.07       92.39
{txt}       3133 {c |}{res}          1        0.07       92.46
{txt}       3150 {c |}{res}          1        0.07       92.52
{txt}       3163 {c |}{res}          1        0.07       92.59
{txt}       3205 {c |}{res}          1        0.07       92.66
{txt}       3209 {c |}{res}          1        0.07       92.72
{txt}       3233 {c |}{res}          1        0.07       92.79
{txt}       3259 {c |}{res}          1        0.07       92.85
{txt}       3273 {c |}{res}          1        0.07       92.92
{txt}       3278 {c |}{res}          1        0.07       92.98
{txt}       3326 {c |}{res}          1        0.07       93.05
{txt}       3328 {c |}{res}          1        0.07       93.11
{txt}       3335 {c |}{res}          1        0.07       93.18
{txt}       3365 {c |}{res}          1        0.07       93.25
{txt}       3445 {c |}{res}          1        0.07       93.31
{txt}       3486 {c |}{res}          1        0.07       93.38
{txt}       3515 {c |}{res}          1        0.07       93.44
{txt}       3551 {c |}{res}          1        0.07       93.51
{txt}       3613 {c |}{res}          1        0.07       93.57
{txt}       3623 {c |}{res}          1        0.07       93.64
{txt}       3660 {c |}{res}          1        0.07       93.70
{txt}       3673 {c |}{res}          1        0.07       93.77
{txt}       3723 {c |}{res}          1        0.07       93.84
{txt}       3724 {c |}{res}          1        0.07       93.90
{txt}       3734 {c |}{res}          1        0.07       93.97
{txt}       3758 {c |}{res}          1        0.07       94.03
{txt}       3934 {c |}{res}          1        0.07       94.10
{txt}       3948 {c |}{res}          1        0.07       94.16
{txt}       3991 {c |}{res}          1        0.07       94.23
{txt}       4225 {c |}{res}          1        0.07       94.30
{txt}       4366 {c |}{res}          1        0.07       94.36
{txt}       4394 {c |}{res}          1        0.07       94.43
{txt}       4451 {c |}{res}          1        0.07       94.49
{txt}       4466 {c |}{res}          1        0.07       94.56
{txt}       4486 {c |}{res}          1        0.07       94.62
{txt}       4527 {c |}{res}          1        0.07       94.69
{txt}       4589 {c |}{res}          1        0.07       94.75
{txt}       4689 {c |}{res}          1        0.07       94.82
{txt}       4775 {c |}{res}          1        0.07       94.89
{txt}       4935 {c |}{res}          1        0.07       94.95
{txt}       4945 {c |}{res}          1        0.07       95.02
{txt}       4971 {c |}{res}          1        0.07       95.08
{txt}       4973 {c |}{res}          1        0.07       95.15
{txt}       5413 {c |}{res}          1        0.07       95.21
{txt}       5453 {c |}{res}          1        0.07       95.28
{txt}       5538 {c |}{res}          1        0.07       95.34
{txt}       5552 {c |}{res}          1        0.07       95.41
{txt}       5557 {c |}{res}          1        0.07       95.48
{txt}       5899 {c |}{res}          1        0.07       95.54
{txt}       5998 {c |}{res}          1        0.07       95.61
{txt}       6062 {c |}{res}          1        0.07       95.67
{txt}       6165 {c |}{res}          1        0.07       95.74
{txt}       6208 {c |}{res}          1        0.07       95.80
{txt}       6349 {c |}{res}          1        0.07       95.87
{txt}       6637 {c |}{res}          1        0.07       95.93
{txt}       7000 {c |}{res}          1        0.07       96.00
{txt}       7200 {c |}{res}          1        0.07       96.07
{txt}       7229 {c |}{res}          1        0.07       96.13
{txt}       7268 {c |}{res}          1        0.07       96.20
{txt}       7404 {c |}{res}          1        0.07       96.26
{txt}       7444 {c |}{res}          1        0.07       96.33
{txt}       7490 {c |}{res}          1        0.07       96.39
{txt}       7829 {c |}{res}          1        0.07       96.46
{txt}       7876 {c |}{res}          1        0.07       96.52
{txt}       7931 {c |}{res}          1        0.07       96.59
{txt}       8067 {c |}{res}          1        0.07       96.66
{txt}       8155 {c |}{res}          1        0.07       96.72
{txt}       8499 {c |}{res}          1        0.07       96.79
{txt}       8689 {c |}{res}          1        0.07       96.85
{txt}       8744 {c |}{res}          1        0.07       96.92
{txt}       9736 {c |}{res}          1        0.07       96.98
{txt}       9819 {c |}{res}          1        0.07       97.05
{txt}       9957 {c |}{res}          1        0.07       97.11
{txt}      11357 {c |}{res}          1        0.07       97.18
{txt}      11369 {c |}{res}          1        0.07       97.25
{txt}      11455 {c |}{res}          1        0.07       97.31
{txt}      11674 {c |}{res}          1        0.07       97.38
{txt}      11994 {c |}{res}          1        0.07       97.44
{txt}      12755 {c |}{res}          1        0.07       97.51
{txt}      13962 {c |}{res}          1        0.07       97.57
{txt}      14549 {c |}{res}          1        0.07       97.64
{txt}      14650 {c |}{res}          1        0.07       97.70
{txt}      14688 {c |}{res}          1        0.07       97.77
{txt}      15835 {c |}{res}          1        0.07       97.84
{txt}      16490 {c |}{res}          1        0.07       97.90
{txt}      17569 {c |}{res}          1        0.07       97.97
{txt}      24379 {c |}{res}          1        0.07       98.03
{txt}      24455 {c |}{res}          1        0.07       98.10
{txt}      26522 {c |}{res}          1        0.07       98.16
{txt}      33247 {c |}{res}          1        0.07       98.23
{txt}      34936 {c |}{res}          1        0.07       98.30
{txt}      38302 {c |}{res}          1        0.07       98.36
{txt}      43616 {c |}{res}          1        0.07       98.43
{txt}      49539 {c |}{res}          1        0.07       98.49
{txt}      54370 {c |}{res}          1        0.07       98.56
{txt}      65646 {c |}{res}          1        0.07       98.62
{txt}      79080 {c |}{res}          1        0.07       98.69
{txt}      87289 {c |}{res}          1        0.07       98.75
{txt}     100959 {c |}{res}          1        0.07       98.82
{txt}     106391 {c |}{res}          1        0.07       98.89
{txt}     110512 {c |}{res}          1        0.07       98.95
{txt}     134644 {c |}{res}          1        0.07       99.02
{txt}     140918 {c |}{res}          1        0.07       99.08
{txt}     142078 {c |}{res}          1        0.07       99.15
{txt}     142426 {c |}{res}          1        0.07       99.21
{txt}     144094 {c |}{res}          1        0.07       99.28
{txt}     148643 {c |}{res}          1        0.07       99.34
{txt}     149439 {c |}{res}          1        0.07       99.41
{txt}     164905 {c |}{res}          1        0.07       99.48
{txt}     176103 {c |}{res}          1        0.07       99.54
{txt}     197368 {c |}{res}          1        0.07       99.61
{txt}     203829 {c |}{res}          1        0.07       99.67
{txt}     266396 {c |}{res}          1        0.07       99.74
{txt}     267508 {c |}{res}          1        0.07       99.80
{txt}     431827 {c |}{res}          1        0.07       99.87
{txt}     439873 {c |}{res}          1        0.07       99.93
{txt}     508750 {c |}{res}          1        0.07      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,525      100.00
{txt}
{com}. gen speed=0
{txt}
{com}. replace speed=1 if durationinseconds<427
{txt}(134 real changes made)

{com}. 
. *compliers
. note exp1a: "The article is about an individual. What is his name?"
{res}{txt}
{com}.         lab def exp1a 1"Alberto" 2"Álvaro" 3"Agustín" 4"Antonio"
{txt}
{com}.         lab val exp1a exp1a
{txt}
{com}. note exp1b: "Did the story mention campaign rallies?"
{res}{txt}
{com}.         lab def exp1b 1"Yes" 2"No"
{txt}
{com}.         lab val exp1b exp1b
{txt}
{com}. gen comply=0
{txt}
{com}.         replace comply=1 if exp1a==3 | exp1b==2
{txt}(779 real changes made)

{com}.         tab exp1a exp1b if comply==0

           {txt}{c |}   EXP1B
     EXP1A {c |}       Yes {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
   Alberto {c |}{res}         4 {txt}{c |}{res}         4 
{txt}    Álvaro {c |}{res}         5 {txt}{c |}{res}         5 
{txt}   Antonio {c |}{res}         2 {txt}{c |}{res}         2 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}        11 {txt}{c |}{res}        11 
{txt}
{com}.         tab exp1a exp1b if comply==1

           {txt}{c |}         EXP1B
     EXP1A {c |}       Yes         No {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
   Alberto {c |}{res}         0          3 {txt}{c |}{res}         3 
{txt}    Álvaro {c |}{res}         0          8 {txt}{c |}{res}         8 
{txt}   Agustín {c |}{res}       235        529 {txt}{c |}{res}       764 
{txt}   Antonio {c |}{res}         0          2 {txt}{c |}{res}         2 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       235        542 {txt}{c |}{res}       777 
{txt}
{com}. 
. *creating treatment variable using one of the timer items (only measured for treated)
. gen treat=.
{txt}(1,709 missing values generated)

{com}. replace treat=1 if q78_firstclick!=.
{txt}(791 real changes made)

{com}. replace treat=0 if q78_firstclick==.
{txt}(918 real changes made)

{com}. 
. 
. ************** analyses
. cd "../figures and tables"
{res}C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables
{txt}
{com}. 
. *** B2. balance check (SM presents t-test results only)
. hotelling age woman ed etid religid, by(treat) 

{txt}{hline}
-> treat = 0

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}        770    2.698701    1.537608          1          6
{txt}{space 7}woman {c |}{res}        770    .4753247    .4997154          0          1
{txt}{space 10}ed {c |}{res}        770    3.668831     .540375          1          4
{txt}{space 8}etid {c |}{res}        770     2.45974    1.625245          1          7
{txt}{space 5}religid {c |}{res}        770    2.338961    1.819034          1          6

{txt}{hline}
-> treat = 1

    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}        746    2.563003    1.478045          1          6
{txt}{space 7}woman {c |}{res}        746    .5254692    .4996859          0          1
{txt}{space 10}ed {c |}{res}        746    3.718499    .5166811          2          4
{txt}{space 8}etid {c |}{res}        746    2.491957    1.667072          1          7
{txt}{space 5}religid {c |}{res}        746    2.368633    1.819221          1          6


{txt}2-group Hotelling's T-squared = {res}10.074082
{txt}F test statistic: {res}((1516-5-1)/(1516-2)(5)) x 10.074082{txt} = {res}2.0094932

{txt}H0: Vectors of means are equal for the two groups
              F({res}5{txt},{res}1510{txt}) = {res}   2.0095
       {txt}Prob > F({res}5{txt},{res}1510{txt}) = {res}   0.0746
{txt}
{com}. // means are different at the .08 level.
. // At the .05 level, we can't reject the hypothesis that the means among 
. // covariates (age, gender, education, ethnic id, regligious id) 
. // are equal for the control and treatment groups.
.         recode etid (1=0)(3/7=0)(2=1), gen(mestizo)
{txt}(1,524 differences between {bf:etid} and {bf:mestizo})

{com}.         recode religid (2/6=0), gen(catholic)
{txt}(664 differences between {bf:religid} and {bf:catholic})

{com}. ttest age, by(treat)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
       0 {c |}{res}{col 12}    918{col 22} 2.666667{col 34} .0504433{col 46} 1.528358{col 58} 2.567669{col 70} 2.765664
       {txt}1 {c |}{res}{col 12}    791{col 22} 2.557522{col 34} .0520835{col 46} 1.464834{col 58} 2.455284{col 70} 2.659761
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}  1,709{col 22}  2.61615{col 34} .0362806{col 46} 1.499843{col 58} 2.544991{col 70} 2.687309
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .1091445{col 34} .0727358{col 58}-.0335161{col 70} .2518052
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}0{txt}) - mean({res}1{txt})                                      t = {res}  1.5006
{txt}H0: diff = 0                                     Degrees of freedom = {res}    1707

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9332         {txt}Pr(|T| > |t|) = {res}0.1337          {txt}Pr(T > t) = {res}0.0668
{txt}
{com}. ttest woman, by(treat)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
       0 {c |}{res}{col 12}    915{col 22} .4885246{col 34} .0165342{col 46} .5001417{col 58} .4560752{col 70} .5209739
       {txt}1 {c |}{res}{col 12}    789{col 22}  .531052{col 34} .0177774{col 46} .4993514{col 58} .4961553{col 70} .5659486
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}  1,704{col 22}  .508216{col 34} .0121145{col 46} .5000793{col 58} .4844552{col 70} .5319768
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.0425274{col 34} .0242807{col 58}-.0901505{col 70} .0050958
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}0{txt}) - mean({res}1{txt})                                      t = {res} -1.7515
{txt}H0: diff = 0                                     Degrees of freedom = {res}    1702

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0400         {txt}Pr(|T| > |t|) = {res}0.0800          {txt}Pr(T > t) = {res}0.9600
{txt}
{com}. ttest ed, by(treat)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
       0 {c |}{res}{col 12}    774{col 22} 3.667959{col 34}  .019421{col 46} .5403096{col 58} 3.629834{col 70} 3.706083
       {txt}1 {c |}{res}{col 12}    749{col 22} 3.718291{col 34} .0188732{col 46} .5165178{col 58}  3.68124{col 70} 3.755342
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}  1,523{col 22} 3.692712{col 34} .0135595{col 46} .5291683{col 58} 3.666114{col 70} 3.719309
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.0503324{col 34} .0271009{col 58}-.1034914{col 70} .0028266
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}0{txt}) - mean({res}1{txt})                                      t = {res} -1.8572
{txt}H0: diff = 0                                     Degrees of freedom = {res}    1521

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0317         {txt}Pr(|T| > |t|) = {res}0.0635          {txt}Pr(T > t) = {res}0.9683
{txt}
{com}. ttest mestizo, by(treat)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
       0 {c |}{res}{col 12}    774{col 22} .6382429{col 34} .0172827{col 46} .4808197{col 58} .6043163{col 70} .6721695
       {txt}1 {c |}{res}{col 12}    750{col 22} .6493333{col 34} .0174357{col 46} .4774972{col 58} .6151046{col 70} .6835621
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}  1,524{col 22} .6437008{col 34} .0122716{col 46} .4790623{col 58} .6196299{col 70} .6677717
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.0110904{col 34} .0245526{col 58}-.0592509{col 70}   .03707
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}0{txt}) - mean({res}1{txt})                                      t = {res} -0.4517
{txt}H0: diff = 0                                     Degrees of freedom = {res}    1522

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.3258         {txt}Pr(|T| > |t|) = {res}0.6515          {txt}Pr(T > t) = {res}0.6742
{txt}
{com}. ttest catholic, by(treat)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
       0 {c |}{res}{col 12}    775{col 22} .5716129{col 34} .0177868{col 46} .4951646{col 58} .5366967{col 70} .6065291
       {txt}1 {c |}{res}{col 12}    749{col 22} .5567423{col 34} .0181637{col 46} .4971018{col 58} .5210844{col 70} .5924002
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}  1,524{col 22} .5643045{col 34} .0127057{col 46} .4960105{col 58}  .539382{col 70}  .589227
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .0148706{col 34} .0254206{col 58}-.0349925{col 70} .0647336
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}0{txt}) - mean({res}1{txt})                                      t = {res}  0.5850
{txt}H0: diff = 0                                     Degrees of freedom = {res}    1522

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.7207         {txt}Pr(|T| > |t|) = {res}0.5586          {txt}Pr(T > t) = {res}0.2793
{txt}
{com}. 
. *** B5. Weighted
. * recoding age, education to match census categories
. gen age2=.
{txt}(1,709 missing values generated)

{com}.         replace age2=6 if birth>64
{txt}(112 real changes made)

{com}.         replace age2=5 if birth<65
{txt}(1,597 real changes made)

{com}.         replace age2=4 if birth<55
{txt}(1,445 real changes made)

{com}.         replace age2=3 if birth<45
{txt}(1,220 real changes made)

{com}.         replace age2=2 if birth<35 
{txt}(873 real changes made)

{com}.         replace age2=1 if birth<25
{txt}(472 real changes made)

{com}.         tab birth age2

           {txt}{c |}                               age2
     Birth {c |}         1          2          3          4          5          6 {c |}     Total
{hline 11}{c +}{hline 66}{c +}{hline 10}
        18 {c |}{res}        57          0          0          0          0          0 {txt}{c |}{res}        57 
{txt}        19 {c |}{res}        55          0          0          0          0          0 {txt}{c |}{res}        55 
{txt}        20 {c |}{res}        82          0          0          0          0          0 {txt}{c |}{res}        82 
{txt}        21 {c |}{res}        55          0          0          0          0          0 {txt}{c |}{res}        55 
{txt}        22 {c |}{res}        79          0          0          0          0          0 {txt}{c |}{res}        79 
{txt}        23 {c |}{res}        78          0          0          0          0          0 {txt}{c |}{res}        78 
{txt}        24 {c |}{res}        66          0          0          0          0          0 {txt}{c |}{res}        66 
{txt}        25 {c |}{res}         0         49          0          0          0          0 {txt}{c |}{res}        49 
{txt}        26 {c |}{res}         0         32          0          0          0          0 {txt}{c |}{res}        32 
{txt}        27 {c |}{res}         0         44          0          0          0          0 {txt}{c |}{res}        44 
{txt}        28 {c |}{res}         0         39          0          0          0          0 {txt}{c |}{res}        39 
{txt}        29 {c |}{res}         0         33          0          0          0          0 {txt}{c |}{res}        33 
{txt}        30 {c |}{res}         0         51          0          0          0          0 {txt}{c |}{res}        51 
{txt}        31 {c |}{res}         0         41          0          0          0          0 {txt}{c |}{res}        41 
{txt}        32 {c |}{res}         0         37          0          0          0          0 {txt}{c |}{res}        37 
{txt}        33 {c |}{res}         0         40          0          0          0          0 {txt}{c |}{res}        40 
{txt}        34 {c |}{res}         0         35          0          0          0          0 {txt}{c |}{res}        35 
{txt}        35 {c |}{res}         0          0         45          0          0          0 {txt}{c |}{res}        45 
{txt}        36 {c |}{res}         0          0         35          0          0          0 {txt}{c |}{res}        35 
{txt}        37 {c |}{res}         0          0         47          0          0          0 {txt}{c |}{res}        47 
{txt}        38 {c |}{res}         0          0         33          0          0          0 {txt}{c |}{res}        33 
{txt}        39 {c |}{res}         0          0         43          0          0          0 {txt}{c |}{res}        43 
{txt}        40 {c |}{res}         0          0         35          0          0          0 {txt}{c |}{res}        35 
{txt}        41 {c |}{res}         0          0         28          0          0          0 {txt}{c |}{res}        28 
{txt}        42 {c |}{res}         0          0         27          0          0          0 {txt}{c |}{res}        27 
{txt}        43 {c |}{res}         0          0         28          0          0          0 {txt}{c |}{res}        28 
{txt}        44 {c |}{res}         0          0         26          0          0          0 {txt}{c |}{res}        26 
{txt}        45 {c |}{res}         0          0          0         33          0          0 {txt}{c |}{res}        33 
{txt}        46 {c |}{res}         0          0          0         37          0          0 {txt}{c |}{res}        37 
{txt}        47 {c |}{res}         0          0          0         20          0          0 {txt}{c |}{res}        20 
{txt}        48 {c |}{res}         0          0          0         21          0          0 {txt}{c |}{res}        21 
{txt}        49 {c |}{res}         0          0          0         26          0          0 {txt}{c |}{res}        26 
{txt}        50 {c |}{res}         0          0          0         18          0          0 {txt}{c |}{res}        18 
{txt}        51 {c |}{res}         0          0          0         23          0          0 {txt}{c |}{res}        23 
{txt}        52 {c |}{res}         0          0          0         16          0          0 {txt}{c |}{res}        16 
{txt}        53 {c |}{res}         0          0          0         19          0          0 {txt}{c |}{res}        19 
{txt}        54 {c |}{res}         0          0          0         12          0          0 {txt}{c |}{res}        12 
{txt}        55 {c |}{res}         0          0          0          0         25          0 {txt}{c |}{res}        25 
{txt}        56 {c |}{res}         0          0          0          0         19          0 {txt}{c |}{res}        19 
{txt}        57 {c |}{res}         0          0          0          0         19          0 {txt}{c |}{res}        19 
{txt}        58 {c |}{res}         0          0          0          0         15          0 {txt}{c |}{res}        15 
{txt}        59 {c |}{res}         0          0          0          0         11          0 {txt}{c |}{res}        11 
{txt}        60 {c |}{res}         0          0          0          0         24          0 {txt}{c |}{res}        24 
{txt}        61 {c |}{res}         0          0          0          0         10          0 {txt}{c |}{res}        10 
{txt}        62 {c |}{res}         0          0          0          0          8          0 {txt}{c |}{res}         8 
{txt}        63 {c |}{res}         0          0          0          0          9          0 {txt}{c |}{res}         9 
{txt}        64 {c |}{res}         0          0          0          0         12          0 {txt}{c |}{res}        12 
{txt}        65 {c |}{res}         0          0          0          0          0         24 {txt}{c |}{res}        24 
{txt}        66 {c |}{res}         0          0          0          0          0         88 {txt}{c |}{res}        88 
{txt}{hline 11}{c +}{hline 66}{c +}{hline 10}
     Total {c |}{res}       472        401        347        225        152        112 {txt}{c |}{res}     1,709 
{txt}
{com}.         lab var age2 "age"
{txt}
{com}.         lab define age2 1"18-24" 2"25-34" 3"35-44" 4"45-54" 5"55-64" 6"65+"
{txt}
{com}.         lab val age2 age2
{txt}
{com}. recode ed(1/2=1 "Primary or lower")(3=2 "Secondary")(4=3 "University or above"), gen(ednew)
{txt}(1,520 differences between {bf:ed} and {bf:ednew})

{com}. 
. * creating a single categorical variable by combining gender, age, and education level
. egen woman_age2_ed2=group(woman age2 ednew), label
{res}{txt}(191 missing values generated)

{com}. 
. * creating the weight, using proportions calculated from the census data 
. ipfweight woman_age2_ed2, gen(weight) val(.0179 .0884 .0063 .0167 .0598 .0232 .0227 .0482 .0182 .0246 .0379 .0134 .0237 .0191 .0099 .0348 .0094 .0063 .0149 .0897 .0077 .0154 .0646 .0265 .0239 .0528 .0203 .0280 .0410 .0143 .0303 .0201 .0088 .0440 .0110 .0043) maxit(100) up(5)

no tolerance criteria specified
Maximum deviation: 10.602 percentage points after 100 iterations

                           {txt}weight
{hline 61}
      Percentiles      Smallest
 1%    {res} .0616175       .0616175
{txt} 5%    {res} .0616175       .0616175
{txt}10%    {res} .0616175       .0616175       {txt}Obs         {res}      1,518
{txt}25%    {res} .1809402       .0616175       {txt}Sum of wgt. {res}      1,518

{txt}50%    {res} .3730151                      {txt}Mean          {res}        1
                        {txt}Largest       Std. dev.     {res} 1.423577
{txt}75%    {res}  1.54091       5.948497
{txt}90%    {res} 2.637551       5.948497       {txt}Variance      {res}  2.02657
{txt}95%    {res} 4.918282       5.948497       {txt}Skewness      {res} 2.007659
{txt}99%    {res} 5.948497       5.948497       {txt}Kurtosis      {res} 6.280571
{txt}
{com}. 
. * creating a second weight, examining each factor individually
. ipfweight ednew age2 woman, gen(wt2) val(.2968 .5420 .1592 .2259 .2066 .1865 .1594 .1119 .1098 .4815 .5185) maxit(100) up(5)

no tolerance criteria specified
Maximum deviation: 65.252 percentage points after 100 iterations

                             {txt}wt2
{hline 61}
      Percentiles      Smallest
 1%    {res} .1414834       .1414834
{txt} 5%    {res} .1669727       .1414834
{txt}10%    {res}  .178078       .1414834       {txt}Obs         {res}      1,518
{txt}25%    {res} .1837053       .1414834       {txt}Sum of wgt. {res}      1,518

{txt}50%    {res} .2488027                      {txt}Mean          {res}        1
                        {txt}Largest       Std. dev.     {res} 1.313068
{txt}75%    {res} 1.790398       5.019852
{txt}90%    {res} 3.031739       5.019852       {txt}Variance      {res} 1.724147
{txt}95%    {res} 3.962825       5.019852       {txt}Skewness      {res} 1.615667
{txt}99%    {res} 5.019852       5.019852       {txt}Kurtosis      {res} 4.656322
{txt}
{com}. 
. * table
. svyset [pw=weight]

{txt}Sampling weights:{col 19}{res}weight
             {txt}VCE:{col 19}{res}linearized
     {txt}Single unit:{col 19}{res}missing
        {txt}Strata 1:{col 19}<one>
 Sampling unit 1:{col 19}<observations>
           FPC 1:{col 19}<zero>
{p2colreset}{...}

{com}. svy: reg b47a treat
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:1}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,515}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:1,515}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,509.9969}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,514}
{txt}{col 51}{lalign 15:F({res:1}, {res:1514})}{col 66} = {res}{ralign 10:2.22}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.1367}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0044}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}treat {c |}{col 14}{res}{space 2}-.2544511{col 26}{space 2} .1708684{col 37}{space 1}   -1.49{col 46}{space 3}0.137{col 54}{space 4} -.589615{col 67}{space 3} .0807127
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.329317{col 26}{space 2} .1138733{col 37}{space 1}   29.24{col 46}{space 3}0.000{col 54}{space 4} 3.105951{col 67}{space 3} 3.552683
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using tab2wt1.doc, dec(2) replace keep(treat) stats(coef se pval) ctitle(Full sample)
{txt}{stata `"shellout using `"tab2wt1.doc"'"':tab2wt1.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "tab2wt1.txt""':seeout}

{com}. svy: reg b47a treat if speed==0
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:1}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,480}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:1,480}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,435.6274}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,479}
{txt}{col 51}{lalign 15:F({res:1}, {res:1479})}{col 66} = {res}{ralign 10:2.83}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0925}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0056}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}treat {c |}{col 14}{res}{space 2}-.2892241{col 26}{space 2} .1717871{col 37}{space 1}   -1.68{col 46}{space 3}0.092{col 54}{space 4}-.6261965{col 67}{space 3} .0477482
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.329359{col 26}{space 2}   .11525{col 37}{space 1}   28.89{col 46}{space 3}0.000{col 54}{space 4} 3.103288{col 67}{space 3}  3.55543
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using tab2wt1.doc, dec(2) append keep(treat) stats(coef se pval) ctitle(No speeders)
{txt}{stata `"shellout using `"tab2wt1.doc"'"':tab2wt1.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "tab2wt1.txt""':seeout}

{com}. svy: reg b47a treat if exp1c!=1
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:1}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,192}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:1,192}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,266.1842}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,191}
{txt}{col 51}{lalign 15:F({res:1}, {res:1191})}{col 66} = {res}{ralign 10:4.37}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0368}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0105}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}treat {c |}{col 14}{res}{space 2}-.4013314{col 26}{space 2} .1919486{col 37}{space 1}   -2.09{col 46}{space 3}0.037{col 54}{space 4}-.7779265{col 67}{space 3}-.0247364
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.329317{col 26}{space 2} .1138835{col 37}{space 1}   29.23{col 46}{space 3}0.000{col 54}{space 4} 3.105882{col 67}{space 3} 3.552752
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using tab2wt1.doc, dec(2) append keep(treat) stats(coef se pval) ctitle(Unsat)
{txt}{stata `"shellout using `"tab2wt1.doc"'"':tab2wt1.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "tab2wt1.txt""':seeout}

{com}. svy: tab ed if !missing(b47a), count
{res}{txt}(running {bf:tabulate} on estimation sample)
{res}
{txt}{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:1}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,515}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:1,515}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,509.9969}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,514}

{txt}{hline 10}{c TT}{hline 11}
Education {c |}
Level     {c |}      count
{hline 10}{c +}{hline 11}
     None {c |}      {res}17.85
  {txt}Primary {c |}      {res}225.9
 {txt}Secondar {c |}      {res}978.3
 {txt}Universi {c |}        {res}288
          {txt}{c |} 
    Total {c |}       {res}1510
{txt}{hline 10}{c BT}{hline 11}
Key: {col 1}   count = {res}Weighted count
{txt}
{com}. svy: tab age if !missing(b47a), count
{res}{txt}(running {bf:tabulate} on estimation sample)
{res}
{txt}{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:1}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,515}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:1,515}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,509.9969}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,514}

{txt}{hline 10}{c TT}{hline 11}
      age {c |}      count
{hline 10}{c +}{hline 11}
    18-25 {c |}      {res}414.2
    {txt}26-35 {c |}      {res}349.3
    {txt}36-45 {c |}        {res}304
    {txt}46-55 {c |}      {res}225.2
    {txt}56-65 {c |}      {res}162.1
      {txt}66+ {c |}      {res}55.21
          {txt}{c |} 
    Total {c |}       {res}1510
{txt}{hline 10}{c BT}{hline 11}
Key: {col 1}   count = {res}Weighted count
{txt}
{com}. svy: tab woman if !missing(b47a), count 
{res}{txt}(running {bf:tabulate} on estimation sample)
{res}
{txt}{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:1}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,515}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:1,515}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,509.9969}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,514}

{txt}{hline 10}{c TT}{hline 11}
RECODE of {c |}
gender    {c |}
(Gender)  {c |}      count
{hline 10}{c +}{hline 11}
        0 {c |}      {res}710.6
        {txt}1 {c |}      {res}799.4
          {txt}{c |} 
    Total {c |}       {res}1510
{txt}{hline 10}{c BT}{hline 11}
Key: {col 1}   count = {res}Weighted count
{txt}
{com}. 
. svyset [pw=wt2] 

{txt}Sampling weights:{col 19}{res}wt2
             {txt}VCE:{col 19}{res}linearized
     {txt}Single unit:{col 19}{res}missing
        {txt}Strata 1:{col 19}<one>
 Sampling unit 1:{col 19}<observations>
           FPC 1:{col 19}<zero>
{p2colreset}{...}

{com}. svy: reg b47a treat
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:1}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,515}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:1,515}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,510.4067}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,514}
{txt}{col 51}{lalign 15:F({res:1}, {res:1514})}{col 66} = {res}{ralign 10:1.84}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.1746}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0034}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}treat {c |}{col 14}{res}{space 2}-.2238405{col 26}{space 2} .1648048{col 37}{space 1}   -1.36{col 46}{space 3}0.175{col 54}{space 4}-.5471104{col 67}{space 3} .0994294
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.324598{col 26}{space 2} .1093971{col 37}{space 1}   30.39{col 46}{space 3}0.000{col 54}{space 4} 3.110013{col 67}{space 3} 3.539184
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using tab2wt2.doc, dec(2) replace keep(treat) stats(coef se pval) ctitle(Full sample)
{txt}{stata `"shellout using `"tab2wt2.doc"'"':tab2wt2.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "tab2wt2.txt""':seeout}

{com}. svy: reg b47a treat if speed==0
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:1}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,480}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:1,480}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,447.3107}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,479}
{txt}{col 51}{lalign 15:F({res:1}, {res:1479})}{col 66} = {res}{ralign 10:2.24}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.1349}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0042}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}treat {c |}{col 14}{res}{space 2}-.2492045{col 26}{space 2}  .166589{col 37}{space 1}   -1.50{col 46}{space 3}0.135{col 54}{space 4}-.5759803{col 67}{space 3} .0775713
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.321013{col 26}{space 2} .1110273{col 37}{space 1}   29.91{col 46}{space 3}0.000{col 54}{space 4} 3.103225{col 67}{space 3} 3.538801
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using tab2wt2.doc, dec(2) append keep(treat) stats(coef se pval) ctitle(No speeders)
{txt}{stata `"shellout using `"tab2wt2.doc"'"':tab2wt2.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "tab2wt2.txt""':seeout}

{com}. svy: reg b47a treat if exp1c!=1
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:1}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,192}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:1,192}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,266.3232}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,191}
{txt}{col 51}{lalign 15:F({res:1}, {res:1191})}{col 66} = {res}{ralign 10:3.99}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0459}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0089}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}treat {c |}{col 14}{res}{space 2}-.3738249{col 26}{space 2} .1870984{col 37}{space 1}   -2.00{col 46}{space 3}0.046{col 54}{space 4}-.7409041{col 67}{space 3}-.0067458
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.324598{col 26}{space 2} .1094069{col 37}{space 1}   30.39{col 46}{space 3}0.000{col 54}{space 4} 3.109947{col 67}{space 3}  3.53925
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using tab2wt2.doc, dec(2) append keep(treat) stats(coef se pval) ctitle(Unsat)
{txt}{stata `"shellout using `"tab2wt2.doc"'"':tab2wt2.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "tab2wt2.txt""':seeout}

{com}. svy: tab ed if !missing(b47a), count
{res}{txt}(running {bf:tabulate} on estimation sample)
{res}
{txt}{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:1}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,515}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:1,515}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,510.4067}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,514}

{txt}{hline 10}{c TT}{hline 11}
Education {c |}
Level     {c |}      count
{hline 10}{c +}{hline 11}
     None {c |}      {res}15.06
  {txt}Primary {c |}      {res}205.8
 {txt}Secondar {c |}      {res}996.4
 {txt}Universi {c |}      {res}293.1
          {txt}{c |} 
    Total {c |}       {res}1510
{txt}{hline 10}{c BT}{hline 11}
Key: {col 1}   count = {res}Weighted count
{txt}
{com}. svy: tab age if !missing(b47a), count
{res}{txt}(running {bf:tabulate} on estimation sample)
{res}
{txt}{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:1}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,515}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:1,515}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,510.4067}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,514}

{txt}{hline 10}{c TT}{hline 11}
      age {c |}      count
{hline 10}{c +}{hline 11}
    18-25 {c |}      {res}388.8
    {txt}26-35 {c |}      {res}300.4
    {txt}36-45 {c |}      {res}288.3
    {txt}46-55 {c |}      {res}229.4
    {txt}56-65 {c |}      {res}177.1
      {txt}66+ {c |}      {res}126.5
          {txt}{c |} 
    Total {c |}       {res}1510
{txt}{hline 10}{c BT}{hline 11}
Key: {col 1}   count = {res}Weighted count
{txt}
{com}. svy: tab woman if !missing(b47a), count 
{res}{txt}(running {bf:tabulate} on estimation sample)
{res}
{txt}{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:1}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,515}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:1,515}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,510.4067}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,514}

{txt}{hline 10}{c TT}{hline 11}
RECODE of {c |}
gender    {c |}
(Gender)  {c |}      count
{hline 10}{c +}{hline 11}
        0 {c |}      {res}731.7
        {txt}1 {c |}      {res}778.7
          {txt}{c |} 
    Total {c |}       {res}1510
{txt}{hline 10}{c BT}{hline 11}
Key: {col 1}   count = {res}Weighted count
{txt}
{com}. 
. 
. fre norm
{res}
{txt}norm
{txt}{hline 13}{hline 1}{c TT}{hline 44}
{txt}        {txt}      {c |}      Freq.    Percent      Valid       Cum.
{txt}{hline 13}{hline 1}{c +}{hline 44}
{txt}Valid   0     {c |}{res}        540      31.60      32.49      32.49
{txt}        .125  {c |}{res}         83       4.86       4.99      37.48
{txt}        .25   {c |}{res}        328      19.19      19.74      57.22
{txt}        .375  {c |}{res}        165       9.65       9.93      67.15
{txt}        .5    {c |}{res}        319      18.67      19.19      86.34
{txt}        .625  {c |}{res}         66       3.86       3.97      90.31
{txt}        .75   {c |}{res}        110       6.44       6.62      96.93
{txt}        .875  {c |}{res}         19       1.11       1.14      98.07
{txt}        1     {c |}{res}         32       1.87       1.93     100.00
{txt}        Total {c |}{res}       1662      97.25     100.00           
{txt}Missing .     {c |}{res}         47       2.75                      
{txt}Total         {c |}{res}       1709     100.00                      
{txt}{hline 13}{hline 1}{c BT}{hline 44}

{com}.         recode norm (0=0 "25%")(.125/.25=1 "50%")(.375/.5=2 "75%")(.625/1=3 "100%"), gen(norm_percentiles)
{txt}(1,122 differences between {bf:norm} and {bf:norm_percentiles})

{com}.         lab var norm_percentiles "Approval of Vote Buying, Percentiles"
{txt}
{com}.         
. * figure 2, weight1
. svyset [pw=weight]

{txt}Sampling weights:{col 19}{res}weight
             {txt}VCE:{col 19}{res}linearized
     {txt}Single unit:{col 19}{res}missing
        {txt}Strata 1:{col 19}<one>
 Sampling unit 1:{col 19}<observations>
           FPC 1:{col 19}<zero>
{p2colreset}{...}

{com}. svy: reg b47a c.norm_percentiles##i.treat
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:1}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,515}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:1,515}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,509.9969}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,514}
{txt}{col 51}{lalign 15:F({res:3}, {res:1512})}{col 66} = {res}{ralign 10:3.17}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0236}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0150}

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}  Linearized
{col 1}                    b47a{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}norm_percentiles {c |}{col 26}{res}{space 2} .0785297{col 38}{space 2} .1070514{col 49}{space 1}    0.73{col 58}{space 3}0.463{col 66}{space 4}-.1314551{col 79}{space 3} .2885145
{txt}{space 17}1.treat {c |}{col 26}{res}{space 2}-.5264307{col 38}{space 2} .2766454{col 49}{space 1}   -1.90{col 58}{space 3}0.057{col 66}{space 4}-1.069079{col 79}{space 3} .0162182
{txt}{space 24} {c |}
treat#c.norm_percentiles {c |}
{space 22}1  {c |}{col 26}{res}{space 2} .1916395{col 38}{space 2} .1534371{col 49}{space 1}    1.25{col 58}{space 3}0.212{col 66}{space 4}-.1093323{col 79}{space 3} .4926114
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.214119{col 38}{space 2} .1993455{col 49}{space 1}   16.12{col 58}{space 3}0.000{col 66}{space 4} 2.823097{col 79}{space 3} 3.605142
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         margins, dydx(treat) at(norm_percentiles=(0 1 2 3))
{res}
{txt}Conditional marginal effects

{col 1}{lalign 16:Number of strata}{col 9} = {res}{ralign 5:1}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,515}
{txt}{col 1}{lalign 16:Number of PSUs}{col 9} = {res}{ralign 5:1,515}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,509.9969}
{txt}{col 1}Model VCE: {res:Linearized}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,514}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.treat}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:3}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.treat     {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.treat      {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.5264307{col 26}{space 2} .2766454{col 37}{space 1}   -1.90{col 46}{space 3}0.057{col 54}{space 4}-1.069079{col 67}{space 3} .0162182
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.3347911{col 26}{space 2} .1821107{col 37}{space 1}   -1.84{col 46}{space 3}0.066{col 54}{space 4}-.6920071{col 67}{space 3} .0224249
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1431516{col 26}{space 2} .1920466{col 37}{space 1}   -0.75{col 46}{space 3}0.456{col 54}{space 4} -.519857{col 67}{space 3} .2335539
{txt}{space 10}4  {c |}{col 14}{res}{space 2}  .048488{col 26}{space 2} .2961171{col 37}{space 1}    0.16{col 46}{space 3}0.870{col 54}{space 4}-.5323553{col 67}{space 3} .6293313
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) ///
>                 ytitle("Effects on Trust in Elections") ///
>                 title("Full Sample")
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:norm_percentiles}{p_end}
{res}{txt}
{com}.         graph save Graph "fig2_full_wt1.gph", replace           
{res}{txt}file {bf:fig2_full_wt1.gph} saved

{com}. svy: reg b47a c.norm_percentiles##i.treat if exp1c!=1 
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:1}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,192}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:1,192}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,266.1842}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,191}
{txt}{col 51}{lalign 15:F({res:3}, {res:1189})}{col 66} = {res}{ralign 10:2.56}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0535}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0160}

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}  Linearized
{col 1}                    b47a{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}norm_percentiles {c |}{col 26}{res}{space 2} .0785297{col 38}{space 2}  .107061{col 49}{space 1}    0.73{col 58}{space 3}0.463{col 66}{space 4}-.1315194{col 79}{space 3} .2885788
{txt}{space 17}1.treat {c |}{col 26}{res}{space 2}-.5785971{col 38}{space 2} .3124816{col 49}{space 1}   -1.85{col 58}{space 3}0.064{col 66}{space 4}-1.191673{col 79}{space 3} .0344786
{txt}{space 24} {c |}
treat#c.norm_percentiles {c |}
{space 22}1  {c |}{col 26}{res}{space 2} .1274698{col 38}{space 2} .1769492{col 49}{space 1}    0.72{col 58}{space 3}0.471{col 66}{space 4}-.2196972{col 79}{space 3} .4746367
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2} 3.214119{col 38}{space 2} .1993634{col 49}{space 1}   16.12{col 58}{space 3}0.000{col 66}{space 4} 2.822977{col 79}{space 3} 3.605262
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         margins, dydx(treat) at(norm_percentiles=(0 1 2 3))
{res}
{txt}Conditional marginal effects

{col 1}{lalign 16:Number of strata}{col 9} = {res}{ralign 5:1}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,192}
{txt}{col 1}{lalign 16:Number of PSUs}{col 9} = {res}{ralign 5:1,192}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,266.1842}
{txt}{col 1}Model VCE: {res:Linearized}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,191}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.treat}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:3}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.treat     {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.treat      {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.5785971{col 26}{space 2} .3124816{col 37}{space 1}   -1.85{col 46}{space 3}0.064{col 54}{space 4}-1.191673{col 67}{space 3} .0344786
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.4511274{col 26}{space 2} .2039443{col 37}{space 1}   -2.21{col 46}{space 3}0.027{col 54}{space 4}-.8512574{col 67}{space 3}-.0509974
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.3236576{col 26}{space 2} .2194626{col 37}{space 1}   -1.47{col 46}{space 3}0.141{col 54}{space 4}-.7542339{col 67}{space 3} .1069187
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.1961878{col 26}{space 2} .3425733{col 37}{space 1}   -0.57{col 46}{space 3}0.567{col 54}{space 4}-.8683021{col 67}{space 3} .4759265
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) ///
>                 ytitle("Effects on Trust in Elections") ///
>                 title("Unsaturated Sample")
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:norm_percentiles}{p_end}
{res}{txt}
{com}.         graph save Graph "fig2_unsat_wt1.gph", replace
{res}{txt}file {bf:fig2_unsat_wt1.gph} saved

{com}. graph combine fig2_full_wt1.gph fig2_unsat_wt1.gph, xsize(8) ysize(4) title("")
{res}{txt}
{com}.         graph save Graph "fig2_wt1.gph", replace
{res}{txt}file {bf:fig2_wt1.gph} saved

{com}.         
. * figure 2, weight2
. svyset [pw=wt2]

{txt}Sampling weights:{col 19}{res}wt2
             {txt}VCE:{col 19}{res}linearized
     {txt}Single unit:{col 19}{res}missing
        {txt}Strata 1:{col 19}<one>
 Sampling unit 1:{col 19}<observations>
           FPC 1:{col 19}<zero>
{p2colreset}{...}

{com}. svy: reg b47a c.norm_percentiles##i.treat
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:1}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,515}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:1,515}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,510.4067}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,514}
{txt}{col 51}{lalign 15:F({res:3}, {res:1512})}{col 66} = {res}{ralign 10:3.32}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0191}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0166}

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}  Linearized
{col 1}                    b47a{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}norm_percentiles {c |}{col 26}{res}{space 2} .1039424{col 38}{space 2} .1021753{col 49}{space 1}    1.02{col 58}{space 3}0.309{col 66}{space 4}-.0964776{col 79}{space 3} .3043625
{txt}{space 17}1.treat {c |}{col 26}{res}{space 2}-.4824555{col 38}{space 2} .2737761{col 49}{space 1}   -1.76{col 58}{space 3}0.078{col 66}{space 4}-1.019476{col 79}{space 3} .0545651
{txt}{space 24} {c |}
treat#c.norm_percentiles {c |}
{space 22}1  {c |}{col 26}{res}{space 2} .1891605{col 38}{space 2} .1494609{col 49}{space 1}    1.27{col 58}{space 3}0.206{col 66}{space 4}-.1040118{col 79}{space 3} .4823329
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}  3.17968{col 38}{space 2} .1877212{col 49}{space 1}   16.94{col 58}{space 3}0.000{col 66}{space 4} 2.811459{col 79}{space 3} 3.547901
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         margins, dydx(treat) at(norm_percentiles=(0 1 2 3))
{res}
{txt}Conditional marginal effects

{col 1}{lalign 16:Number of strata}{col 9} = {res}{ralign 5:1}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,515}
{txt}{col 1}{lalign 16:Number of PSUs}{col 9} = {res}{ralign 5:1,515}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,510.4067}
{txt}{col 1}Model VCE: {res:Linearized}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,514}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.treat}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:3}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.treat     {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.treat      {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.4824555{col 26}{space 2} .2737761{col 37}{space 1}   -1.76{col 46}{space 3}0.078{col 54}{space 4}-1.019476{col 67}{space 3} .0545651
{txt}{space 10}2  {c |}{col 14}{res}{space 2} -.293295{col 26}{space 2} .1775364{col 37}{space 1}   -1.65{col 46}{space 3}0.099{col 54}{space 4}-.6415382{col 67}{space 3} .0549483
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1041344{col 26}{space 2} .1810029{col 37}{space 1}   -0.58{col 46}{space 3}0.565{col 54}{space 4}-.4591775{col 67}{space 3} .2509087
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0850261{col 26}{space 2} .2805033{col 37}{space 1}    0.30{col 46}{space 3}0.762{col 54}{space 4}  -.46519{col 67}{space 3} .6352423
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) ///
>                 ytitle("Effects on Trust in Elections") ///
>                 title("Full Sample")
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:norm_percentiles}{p_end}
{res}{txt}
{com}.         graph save Graph "fig2_full_wt2.gph", replace           
{res}{txt}file {bf:fig2_full_wt2.gph} saved

{com}. svy: reg b47a c.norm_percentiles##i.treat if exp1c!=1 
{res}{txt}(running {bf:regress} on estimation sample)
{res}
{txt}Survey: Linear regression

{col 1}{lalign 16:Number of strata}{col 17} = {res}{ralign 5:1}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,192}
{txt}{col 1}{lalign 16:Number of PSUs}{col 17} = {res}{ralign 5:1,192}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,266.3232}
{txt}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,191}
{txt}{col 51}{lalign 15:F({res:3}, {res:1189})}{col 66} = {res}{ralign 10:2.28}
{txt}{col 51}{lalign 15:Prob > F}{col 66} = {res}{ralign 10:0.0781}
{txt}{col 51}{lalign 15:R-squared}{col 66} = {res}{ralign 10:0.0153}

{txt}{hline 25}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 26}{c |}{col 38}  Linearized
{col 1}                    b47a{col 26}{c |} Coefficient{col 38}  std. err.{col 50}      t{col 58}   P>|t|{col 66}     [95% con{col 79}f. interval]
{hline 25}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}norm_percentiles {c |}{col 26}{res}{space 2} .1039424{col 38}{space 2} .1021844{col 49}{space 1}    1.02{col 58}{space 3}0.309{col 66}{space 4} -.096539{col 79}{space 3} .3044239
{txt}{space 17}1.treat {c |}{col 26}{res}{space 2}-.4997089{col 38}{space 2} .3183946{col 49}{space 1}   -1.57{col 58}{space 3}0.117{col 66}{space 4}-1.124386{col 79}{space 3} .1249678
{txt}{space 24} {c |}
treat#c.norm_percentiles {c |}
{space 22}1  {c |}{col 26}{res}{space 2} .0958876{col 38}{space 2} .1757155{col 49}{space 1}    0.55{col 58}{space 3}0.585{col 66}{space 4}-.2488588{col 79}{space 3}  .440634
{txt}{space 24} {c |}
{space 19}_cons {c |}{col 26}{res}{space 2}  3.17968{col 38}{space 2}  .187738{col 49}{space 1}   16.94{col 58}{space 3}0.000{col 66}{space 4} 2.811346{col 79}{space 3} 3.548014
{txt}{hline 25}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         margins, dydx(treat) at(norm_percentiles=(0 1 2 3))
{res}
{txt}Conditional marginal effects

{col 1}{lalign 16:Number of strata}{col 9} = {res}{ralign 5:1}{txt}{col 51}{lalign 15:Number of obs}{col 66} = {res}{ralign 10:1,192}
{txt}{col 1}{lalign 16:Number of PSUs}{col 9} = {res}{ralign 5:1,192}{txt}{col 51}{lalign 15:Population size}{col 66} = {res}{ralign 10:1,266.3232}
{txt}{col 1}Model VCE: {res:Linearized}{col 51}{lalign 15:Design df}{col 66} = {res}{ralign 10:1,191}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.treat}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:1}}
{lalign 7:3._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:2}}
{lalign 7:4._at: }{space 0}{lalign 16:norm_percentiles} = {res:{ralign 1:3}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.treat     {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.treat      {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.4997089{col 26}{space 2} .3183946{col 37}{space 1}   -1.57{col 46}{space 3}0.117{col 54}{space 4}-1.124386{col 67}{space 3} .1249678
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.4038213{col 26}{space 2} .2032211{col 37}{space 1}   -1.99{col 46}{space 3}0.047{col 54}{space 4}-.8025326{col 67}{space 3}-.0051101
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.3079337{col 26}{space 2} .2073027{col 37}{space 1}   -1.49{col 46}{space 3}0.138{col 54}{space 4}-.7146529{col 67}{space 3} .0987855
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.2120462{col 26}{space 2}  .326193{col 37}{space 1}   -0.65{col 46}{space 3}0.516{col 54}{space 4}-.8520231{col 67}{space 3} .4279308
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) ///
>                 ytitle("Effects on Trust in Elections") ///
>                 title("Unsaturated Sample")
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:norm_percentiles}{p_end}
{res}{txt}
{com}.         graph save Graph "fig2_unsat_wt2.gph", replace
{res}{txt}file {bf:fig2_unsat_wt2.gph} saved

{com}. graph combine fig2_full_wt2.gph fig2_unsat_wt2.gph, xsize(8) ysize(4) title("")
{res}{txt}
{com}.         graph save Graph "fig2_wt2.gph", replace
{res}{txt}file {bf:fig2_wt2.gph} saved

{com}. 
. 
. *** B6. excluding non-compliers
. reg b47a treat if comply==1
{txt}{p 0 6 2}note: {bf:treat} omitted because of collinearity.{p_end}

      Source {c |}       SS           df       MS      Number of obs   ={res}       771
{txt}{hline 13}{c +}{hline 34}   F(0, 770)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 2927.73022       770  3.80224704   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 2927.73022       770  3.80224704   {txt}Root MSE        =   {res} 1.9499

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}treat {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2} 3.145266{col 26}{space 2} .0702252{col 37}{space 1}   44.79{col 46}{space 3}0.000{col 54}{space 4}  3.00741{col 67}{space 3} 3.283121
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using b6.doc, dec(2) replace keep(treat) stats(coef se pval) ctitle(Full sample)
{txt}{stata `"shellout using `"b6.doc"'"':b6.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b6.txt""':seeout}

{com}. reg b47a treat if comply==1 & speed==0
{txt}{p 0 6 2}note: {bf:treat} omitted because of collinearity.{p_end}

      Source {c |}       SS           df       MS      Number of obs   ={res}       754
{txt}{hline 13}{c +}{hline 34}   F(0, 753)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res}  2835.2626       753  3.76528898   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res}  2835.2626       753  3.76528898   {txt}Root MSE        =   {res} 1.9404

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}treat {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2} 3.129973{col 26}{space 2} .0706665{col 37}{space 1}   44.29{col 46}{space 3}0.000{col 54}{space 4} 2.991247{col 67}{space 3}   3.2687
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using b6.doc, dec(2) append keep(treat) stats(coef se pval) ctitle(No speeders)
{txt}{stata `"shellout using `"b6.doc"'"':b6.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b6.txt""':seeout}

{com}. reg b47a treat if comply==1 & exp1c!=1
{txt}{p 0 6 2}note: {bf:treat} omitted because of collinearity.{p_end}

      Source {c |}       SS           df       MS      Number of obs   ={res}       440
{txt}{hline 13}{c +}{hline 34}   F(0, 439)       = {res}     0.00
{txt}       Model {c |} {res}          0         0           .   {txt}Prob > F        ={res}         .
{txt}    Residual {c |} {res} 1537.67273       439  3.50267136   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0000
{txt}       Total {c |} {res} 1537.67273       439  3.50267136   {txt}Root MSE        =   {res} 1.8715

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}treat {c |}{col 14}{res}{space 2}        0{col 26}{txt}  (omitted)
{space 7}_cons {c |}{col 14}{res}{space 2} 3.027273{col 26}{space 2} .0892223{col 37}{space 1}   33.93{col 46}{space 3}0.000{col 54}{space 4} 2.851917{col 67}{space 3} 3.202629
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using b6.doc, dec(2) append keep(treat) stats(coef se pval) ctitle(Unsaturated sample)
{txt}{stata `"shellout using `"b6.doc"'"':b6.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b6.txt""':seeout}

{com}. 
. 
. *** B7. unsaturated vs saturated
. ttest b47b, by(exp1c)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
       0 {c |}{res}{col 12}    453{col 22} 3.719647{col 34} .0916367{col 46} 1.950377{col 58}  3.53956{col 70} 3.899734
       {txt}1 {c |}{res}{col 12}    335{col 22} 3.614925{col 34} .1075123{col 46} 1.967799{col 58} 3.403439{col 70} 3.826412
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}    788{col 22} 3.675127{col 34} .0697238{col 46} 1.957241{col 58}  3.53826{col 70} 3.811994
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .1047214{col 34} .1410781{col 58}-.1722131{col 70}  .381656
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}0{txt}) - mean({res}1{txt})                                      t = {res}  0.7423
{txt}H0: diff = 0                                     Degrees of freedom = {res}     786

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.7709         {txt}Pr(|T| > |t|) = {res}0.4581          {txt}Pr(T > t) = {res}0.2291
{txt}
{com}. reg b47a treat

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,590
{txt}{hline 13}{c +}{hline 34}   F(1, 1588)      = {res}     2.20
{txt}       Model {c |} {res} 8.29128473         1  8.29128473   {txt}Prob > F        ={res}    0.1383
{txt}    Residual {c |} {res} 5986.36846     1,588  3.76975344   {txt}R-squared       ={res}    0.0014
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0008
{txt}       Total {c |} {res} 5994.65975     1,589  3.77259896   {txt}Root MSE        =   {res} 1.9416

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}treat {c |}{col 14}{res}{space 2}-.1444443{col 26}{space 2} .0973971{col 37}{space 1}   -1.48{col 46}{space 3}0.138{col 54}{space 4}-.3354847{col 67}{space 3} .0465961
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.309406{col 26}{space 2} .0683047{col 37}{space 1}   48.45{col 46}{space 3}0.000{col 54}{space 4} 3.175429{col 67}{space 3} 3.443383
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using b7.doc, dec(2) replace ctitle(Full sample, no controls)
{txt}{stata `"shellout using `"b7.doc"'"':b7.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b7.txt""':seeout}

{com}. reg b47a treat if exp1c!=1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,255
{txt}{hline 13}{c +}{hline 34}   F(1, 1253)      = {res}     5.34
{txt}       Model {c |} {res} 19.4828648         1  19.4828648   {txt}Prob > F        ={res}    0.0211
{txt}    Residual {c |} {res} 4575.56574     1,253  3.65168854   {txt}R-squared       ={res}    0.0042
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0034
{txt}       Total {c |} {res} 4595.04861     1,254  3.66431308   {txt}Root MSE        =   {res} 1.9109

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}treat {c |}{col 14}{res}{space 2}-.2601889{col 26}{space 2} .1126443{col 37}{space 1}   -2.31{col 46}{space 3}0.021{col 54}{space 4}-.4811812{col 67}{space 3}-.0391967
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.309406{col 26}{space 2} .0672266{col 37}{space 1}   49.23{col 46}{space 3}0.000{col 54}{space 4} 3.177517{col 67}{space 3} 3.441295
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using b7.doc, dec(2) append ctitle(Unsaturated, no controls)
{txt}{stata `"shellout using `"b7.doc"'"':b7.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b7.txt""':seeout}

{com}. reg b47a treat ed mestizo

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,519
{txt}{hline 13}{c +}{hline 34}   F(3, 1515)      = {res}     1.66
{txt}       Model {c |} {res} 18.6363723         3  6.21212408   {txt}Prob > F        ={res}    0.1740
{txt}    Residual {c |} {res} 5674.04434     1,515  3.74524379   {txt}R-squared       ={res}    0.0033
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0013
{txt}       Total {c |} {res} 5692.68071     1,518  3.75011905   {txt}Root MSE        =   {res} 1.9353

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}treat {c |}{col 14}{res}{space 2}-.1577581{col 26}{space 2} .0994494{col 37}{space 1}   -1.59{col 46}{space 3}0.113{col 54}{space 4}-.3528311{col 67}{space 3} .0373149
{txt}{space 10}ed {c |}{col 14}{res}{space 2}-.0257736{col 26}{space 2} .0947262{col 37}{space 1}   -0.27{col 46}{space 3}0.786{col 54}{space 4}-.2115819{col 67}{space 3} .1600347
{txt}{space 5}mestizo {c |}{col 14}{res}{space 2} .1629117{col 26}{space 2} .1041741{col 37}{space 1}    1.56{col 46}{space 3}0.118{col 54}{space 4}-.0414291{col 67}{space 3} .3672524
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.304807{col 26}{space 2} .3544588{col 37}{space 1}    9.32{col 46}{space 3}0.000{col 54}{space 4} 2.609525{col 67}{space 3} 4.000089
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using b7.doc, dec(2) append ctitle(Full sample, controls)
{txt}{stata `"shellout using `"b7.doc"'"':b7.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b7.txt""':seeout}

{com}. reg b47a treat ed mestizo if exp1c!=1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,194
{txt}{hline 13}{c +}{hline 34}   F(3, 1190)      = {res}     2.24
{txt}       Model {c |} {res} 24.3450448         3  8.11501494   {txt}Prob > F        ={res}    0.0821
{txt}    Residual {c |} {res} 4313.90621     1,190  3.62513127   {txt}R-squared       ={res}    0.0056
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0031
{txt}       Total {c |} {res} 4338.25126     1,193  3.63642184   {txt}Root MSE        =   {res}  1.904

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}treat {c |}{col 14}{res}{space 2}-.2756346{col 26}{space 2} .1153822{col 37}{space 1}   -2.39{col 46}{space 3}0.017{col 54}{space 4}-.5020099{col 67}{space 3}-.0492593
{txt}{space 10}ed {c |}{col 14}{res}{space 2}-.0260087{col 26}{space 2} .1016403{col 37}{space 1}   -0.26{col 46}{space 3}0.798{col 54}{space 4}-.2254229{col 67}{space 3} .1734055
{txt}{space 5}mestizo {c |}{col 14}{res}{space 2} .1050131{col 26}{space 2} .1144554{col 37}{space 1}    0.92{col 46}{space 3}0.359{col 54}{space 4}-.1195437{col 67}{space 3} .3295699
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  3.34267{col 26}{space 2} .3802409{col 37}{space 1}    8.79{col 46}{space 3}0.000{col 54}{space 4} 2.596653{col 67}{space 3} 4.088687
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using b7.doc, dec(2) append ctitle(Unsaturated, controls)
{txt}{stata `"shellout using `"b7.doc"'"':b7.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b7.txt""':seeout}

{com}.         
. 
. *** B8. approval, exposure to vb, and trust in elections
. reg b47a c.norm##i.treat

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,590
{txt}{hline 13}{c +}{hline 34}   F(3, 1586)      = {res}     8.46
{txt}       Model {c |} {res} 94.4525027         3  31.4841676   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 5900.20725     1,586  3.72018111   {txt}R-squared       ={res}    0.0158
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0139
{txt}       Total {c |} {res} 5994.65975     1,589  3.77259896   {txt}Root MSE        =   {res} 1.9288

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}norm {c |}{col 14}{res}{space 2} .6421326{col 26}{space 2} .2581949{col 37}{space 1}    2.49{col 46}{space 3}0.013{col 54}{space 4} .1356934{col 67}{space 3} 1.148572
{txt}{space 5}1.treat {c |}{col 14}{res}{space 2}-.2758858{col 26}{space 2} .1438896{col 37}{space 1}   -1.92{col 46}{space 3}0.055{col 54}{space 4}-.5581197{col 67}{space 3} .0063481
{txt}{space 12} {c |}
treat#c.norm {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .4382625{col 26}{space 2} .3680035{col 37}{space 1}    1.19{col 46}{space 3}0.234{col 54}{space 4}-.2835619{col 67}{space 3} 1.160087
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 3.125329{col 26}{space 2} .1004114{col 37}{space 1}   31.13{col 46}{space 3}0.000{col 54}{space 4} 2.928376{col 67}{space 3} 3.322282
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using b8.doc, dec(2) replace keep(norm 1.treat 1.treat#c.norm) stats(coef se pval) ctitle(Full sample)
{txt}{stata `"shellout using `"b8.doc"'"':b8.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b8.txt""':seeout}

{com}. reg b47a c.norm##i.treat if speed==0

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,544
{txt}{hline 13}{c +}{hline 34}   F(3, 1540)      = {res}     8.00
{txt}       Model {c |} {res} 88.8169643         3  29.6056548   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res}  5695.9855     1,540  3.69869188   {txt}R-squared       ={res}    0.0154
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0134
{txt}       Total {c |} {res} 5784.80246     1,543  3.74906187   {txt}Root MSE        =   {res} 1.9232

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}norm {c |}{col 14}{res}{space 2} .6402052{col 26}{space 2} .2682188{col 37}{space 1}    2.39{col 46}{space 3}0.017{col 54}{space 4} .1140926{col 67}{space 3} 1.166318
{txt}{space 5}1.treat {c |}{col 14}{res}{space 2}-.2727742{col 26}{space 2} .1451556{col 37}{space 1}   -1.88{col 46}{space 3}0.060{col 54}{space 4}-.5574977{col 67}{space 3} .0119494
{txt}{space 12} {c |}
treat#c.norm {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .4323066{col 26}{space 2} .3780757{col 37}{space 1}    1.14{col 46}{space 3}0.253{col 54}{space 4}-.3092911{col 67}{space 3} 1.173904
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2}  3.11414{col 26}{space 2}  .101702{col 37}{space 1}   30.62{col 46}{space 3}0.000{col 54}{space 4} 2.914651{col 67}{space 3} 3.313629
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using b8.doc, dec(2) append keep(norm 1.treat 1.treat#c.norm) stats(coef se pval) ctitle(No speeders)
{err}file using "b8.doc" is read-only; cannot be modified or erased
The file needs to be closed if being used by another software such as Word.
{txt}{search r(608), local:r(608);}

end of do-file

{search r(608), local:r(608);}

{com}. do "C:\Users\euiyo\AppData\Local\Temp\STD49a8_000000.tmp"
{txt}
{com}.         outreg2 using b8.doc, dec(2) append keep(norm 1.treat 1.treat#c.norm) stats(coef se pval) ctitle(No speeders)
{txt}{stata `"shellout using `"b8.doc"'"':b8.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b8.txt""':seeout}

{com}. 
{txt}end of do-file

{com}. do "C:\Users\euiyo\AppData\Local\Temp\STD49a8_000000.tmp"
{txt}
{com}. reg b47a c.norm##i.treat if exp1c!=1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,255
{txt}{hline 13}{c +}{hline 34}   F(3, 1251)      = {res}     7.13
{txt}       Model {c |} {res}  77.230991         3  25.7436637   {txt}Prob > F        ={res}    0.0001
{txt}    Residual {c |} {res} 4517.81761     1,251    3.611365   {txt}R-squared       ={res}    0.0168
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0144
{txt}       Total {c |} {res} 4595.04861     1,254  3.66431308   {txt}Root MSE        =   {res} 1.9004

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        b47a{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}norm {c |}{col 14}{res}{space 2} .6421326{col 26}{space 2} .2543908{col 37}{space 1}    2.52{col 46}{space 3}0.012{col 54}{space 4}  .143053{col 67}{space 3} 1.141212
{txt}{space 5}1.treat {c |}{col 14}{res}{space 2}-.3990058{col 26}{space 2} .1694271{col 37}{space 1}   -2.36{col 46}{space 3}0.019{col 54}{space 4}-.7313985{col 67}{space 3}-.0666132
{txt}{space 12} {c |}
treat#c.norm {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .4482049{col 26}{space 2} .4339424{col 37}{space 1}    1.03{col 46}{space 3}0.302{col 54}{space 4}-.4031302{col 67}{space 3}  1.29954
{txt}{space 12} {c |}
{space 7}_cons {c |}{col 14}{res}{space 2} 3.125329{col 26}{space 2}  .098932{col 37}{space 1}   31.59{col 46}{space 3}0.000{col 54}{space 4} 2.931238{col 67}{space 3}  3.31942
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using b8.doc, dec(2) append keep(norm 1.treat 1.treat#c.norm) stats(coef se pval) ctitle(Unsaturated)
{txt}{stata `"shellout using `"b8.doc"'"':b8.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b8.txt""':seeout}

{com}. 
. 
. *** B9. approval, exposure to vb, and trust in elections (disaggregated)
. rescale norms1 0 1
{txt}variable {bf}{res}norms1{sf}{txt} was {bf}{res}byte{sf}{txt} now {bf}{res}float{sf}
{txt}(1,662 real changes made)

{com}. reg b47a c.norms1##i.treat

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,590
{txt}{hline 13}{c +}{hline 34}   F(3, 1586)      = {res}     7.30
{txt}       Model {c |} {res} 81.6316766         3  27.2105589   {txt}Prob > F        ={res}    0.0001
{txt}    Residual {c |} {res} 5913.02807     1,586  3.72826486   {txt}R-squared       ={res}    0.0136
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0118
{txt}       Total {c |} {res} 5994.65975     1,589  3.77259896   {txt}Root MSE        =   {res} 1.9309

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          b47a{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}norms1 {c |}{col 16}{res}{space 2} .5200961{col 28}{space 2}  .243368{col 39}{space 1}    2.14{col 48}{space 3}0.033{col 56}{space 4} .0427394{col 69}{space 3} .9974529
{txt}{space 7}1.treat {c |}{col 16}{res}{space 2}-.2729099{col 28}{space 2} .1337672{col 39}{space 1}   -2.04{col 48}{space 3}0.041{col 56}{space 4} -.535289{col 69}{space 3}-.0105309
{txt}{space 14} {c |}
treat#c.norms1 {c |}
{space 12}1  {c |}{col 16}{res}{space 2} .4420151{col 28}{space 2} .3471486{col 39}{space 1}    1.27{col 48}{space 3}0.203{col 56}{space 4}-.2389033{col 69}{space 3} 1.122934
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2} 3.175198{col 28}{space 2} .0925095{col 39}{space 1}   34.32{col 48}{space 3}0.000{col 56}{space 4} 2.993744{col 69}{space 3} 3.356652
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. outreg2 using b9_1.doc, dec(2) replace keep(norms1 1.treat 1.treat#c.norms1) stats(coef se pval) ctitle(Full sample)
{txt}{stata `"shellout using `"b9_1.doc"'"':b9_1.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b9_1.txt""':seeout}

{com}.         margins, dydx(treat) at(norms1=(0(.25)1))
{res}
{txt}{col 1}Conditional marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,590}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.treat}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 6:norms1} = {res:{ralign 3:0}}
{lalign 7:2._at: }{space 0}{lalign 6:norms1} = {res:{ralign 3:.25}}
{lalign 7:3._at: }{space 0}{lalign 6:norms1} = {res:{ralign 3:.5}}
{lalign 7:4._at: }{space 0}{lalign 6:norms1} = {res:{ralign 3:.75}}
{lalign 7:5._at: }{space 0}{lalign 6:norms1} = {res:{ralign 3:1}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.treat     {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.treat      {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.2729099{col 26}{space 2} .1337672{col 37}{space 1}   -2.04{col 46}{space 3}0.041{col 54}{space 4} -.535289{col 67}{space 3}-.0105309
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.1624061{col 26}{space 2} .0970469{col 37}{space 1}   -1.67{col 46}{space 3}0.094{col 54}{space 4}-.3527598{col 67}{space 3} .0279475
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0519024{col 26}{space 2}  .126517{col 37}{space 1}   -0.41{col 46}{space 3}0.682{col 54}{space 4}-.3000605{col 67}{space 3} .1962558
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0586014{col 26}{space 2} .1940594{col 37}{space 1}    0.30{col 46}{space 3}0.763{col 54}{space 4}-.3220384{col 67}{space 3} .4392413
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .1691052{col 26}{space 2} .2727188{col 37}{space 1}    0.62{col 46}{space 3}0.535{col 54}{space 4} -.365822{col 67}{space 3} .7040324
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) xtitle("Approval of Vote Buying (party-centered)") ytitle(Predicted Trust in Elections) title(Full Sample) 
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:norms1}{p_end}
{res}{txt}
{com}.         graph save Graph "b9_1_full.gph", replace
{res}{txt}file {bf:b9_1_full.gph} saved

{com}. reg b47a c.norms1##i.treat if speed==0

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,544
{txt}{hline 13}{c +}{hline 34}   F(3, 1540)      = {res}     6.68
{txt}       Model {c |} {res} 74.2748688         3  24.7582896   {txt}Prob > F        ={res}    0.0002
{txt}    Residual {c |} {res} 5710.52759     1,540   3.7081348   {txt}R-squared       ={res}    0.0128
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0109
{txt}       Total {c |} {res} 5784.80246     1,543  3.74906187   {txt}Root MSE        =   {res} 1.9257

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          b47a{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}norms1 {c |}{col 16}{res}{space 2} .5125364{col 28}{space 2} .2524429{col 39}{space 1}    2.03{col 48}{space 3}0.042{col 56}{space 4} .0173682{col 69}{space 3} 1.007705
{txt}{space 7}1.treat {c |}{col 16}{res}{space 2}-.2658981{col 28}{space 2} .1347755{col 39}{space 1}   -1.97{col 48}{space 3}0.049{col 56}{space 4} -.530261{col 69}{space 3}-.0015352
{txt}{space 14} {c |}
treat#c.norms1 {c |}
{space 12}1  {c |}{col 16}{res}{space 2} .4214752{col 28}{space 2} .3562989{col 39}{space 1}    1.18{col 48}{space 3}0.237{col 56}{space 4}-.2774071{col 69}{space 3} 1.120357
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2} 3.164713{col 28}{space 2} .0934498{col 39}{space 1}   33.87{col 48}{space 3}0.000{col 56}{space 4} 2.981411{col 69}{space 3} 3.348016
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using b9_1.doc, dec(2) append keep(norms1 1.treat 1.treat#c.norms1) stats(coef se pval) ctitle(No speeders)
{txt}{stata `"shellout using `"b9_1.doc"'"':b9_1.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b9_1.txt""':seeout}

{com}. reg b47a c.norms1##i.treat if exp1c!=1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,255
{txt}{hline 13}{c +}{hline 34}   F(3, 1251)      = {res}     6.60
{txt}       Model {c |} {res}  71.645373         3   23.881791   {txt}Prob > F        ={res}    0.0002
{txt}    Residual {c |} {res} 4523.40323     1,251  3.61582992   {txt}R-squared       ={res}    0.0156
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0132
{txt}       Total {c |} {res} 4595.04861     1,254  3.66431308   {txt}Root MSE        =   {res} 1.9015

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          b47a{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}norms1 {c |}{col 16}{res}{space 2} .5200961{col 28}{space 2} .2396702{col 39}{space 1}    2.17{col 48}{space 3}0.030{col 56}{space 4} .0498963{col 69}{space 3}  .990296
{txt}{space 7}1.treat {c |}{col 16}{res}{space 2}-.4100111{col 28}{space 2} .1571345{col 39}{space 1}   -2.61{col 48}{space 3}0.009{col 56}{space 4}-.7182873{col 69}{space 3}-.1017348
{txt}{space 14} {c |}
treat#c.norms1 {c |}
{space 12}1  {c |}{col 16}{res}{space 2} .5017265{col 28}{space 2}  .406072{col 39}{space 1}    1.24{col 48}{space 3}0.217{col 56}{space 4}-.2949308{col 69}{space 3} 1.298384
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2} 3.175198{col 28}{space 2} .0911039{col 39}{space 1}   34.85{col 48}{space 3}0.000{col 56}{space 4} 2.996465{col 69}{space 3} 3.353931
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using b9_1.doc, dec(2) append keep(norms1 1.treat 1.treat#c.norms1) stats(coef se pval) ctitle(Unsaturated)
{txt}{stata `"shellout using `"b9_1.doc"'"':b9_1.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b9_1.txt""':seeout}

{com}.         margins, dydx(treat) at(norms1=(0(.25)1))
{res}
{txt}{col 1}Conditional marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,255}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.treat}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 6:norms1} = {res:{ralign 3:0}}
{lalign 7:2._at: }{space 0}{lalign 6:norms1} = {res:{ralign 3:.25}}
{lalign 7:3._at: }{space 0}{lalign 6:norms1} = {res:{ralign 3:.5}}
{lalign 7:4._at: }{space 0}{lalign 6:norms1} = {res:{ralign 3:.75}}
{lalign 7:5._at: }{space 0}{lalign 6:norms1} = {res:{ralign 3:1}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.treat     {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.treat      {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.4100111{col 26}{space 2} .1571345{col 37}{space 1}   -2.61{col 46}{space 3}0.009{col 54}{space 4}-.7182873{col 67}{space 3}-.1017348
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.2845795{col 26}{space 2} .1124806{col 37}{space 1}   -2.53{col 46}{space 3}0.012{col 54}{space 4}-.5052509{col 67}{space 3} -.063908
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.1591478{col 26}{space 2} .1456857{col 37}{space 1}   -1.09{col 46}{space 3}0.275{col 54}{space 4}-.4449631{col 67}{space 3} .1266675
{txt}{space 10}4  {c |}{col 14}{res}{space 2}-.0337162{col 26}{space 2} .2245186{col 37}{space 1}   -0.15{col 46}{space 3}0.881{col 54}{space 4}-.4741907{col 67}{space 3} .4067582
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .0917154{col 26}{space 2} .3165512{col 37}{space 1}    0.29{col 46}{space 3}0.772{col 54}{space 4}-.5293143{col 67}{space 3} .7127451
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) xtitle("Approval of Vote Buying (party-centered)") ytitle(Predicted Trust in Elections) title(Unsaturated Sample)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:norms1}{p_end}
{res}{txt}
{com}.         graph save Graph "b9_1_unsat.gph", replace
{res}{txt}file {bf:b9_1_unsat.gph} saved

{com}. graph combine b9_1_full.gph b9_1_unsat.gph
{res}{txt}
{com}.         graph save Graph "b9_1.gph", replace
{res}{txt}file {bf:b9_1.gph} saved

{com}. 
. rescale norms2 0 1
{txt}variable {bf}{res}norms2{sf}{txt} was {bf}{res}byte{sf}{txt} now {bf}{res}float{sf}
{txt}(1,662 real changes made)

{com}. reg b47a c.norms2##i.treat

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,590
{txt}{hline 13}{c +}{hline 34}   F(3, 1586)      = {res}     7.92
{txt}       Model {c |} {res} 88.5133885         3  29.5044628   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 5906.14636     1,586  3.72392583   {txt}R-squared       ={res}    0.0148
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0129
{txt}       Total {c |} {res} 5994.65975     1,589  3.77259896   {txt}Root MSE        =   {res} 1.9297

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          b47a{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}norms2 {c |}{col 16}{res}{space 2} .6204418{col 28}{space 2} .2436213{col 39}{space 1}    2.55{col 48}{space 3}0.011{col 56}{space 4} .1425881{col 69}{space 3} 1.098296
{txt}{space 7}1.treat {c |}{col 16}{res}{space 2}-.2476119{col 28}{space 2} .1456015{col 39}{space 1}   -1.70{col 48}{space 3}0.089{col 56}{space 4}-.5332035{col 69}{space 3} .0379797
{txt}{space 14} {c |}
treat#c.norms2 {c |}
{space 12}1  {c |}{col 16}{res}{space 2}  .340087{col 28}{space 2} .3473162{col 39}{space 1}    0.98{col 48}{space 3}0.328{col 56}{space 4}-.3411602{col 69}{space 3} 1.021334
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}  3.11379{col 28}{space 2} .1025115{col 39}{space 1}   30.38{col 48}{space 3}0.000{col 56}{space 4} 2.912718{col 69}{space 3} 3.314862
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. outreg2 using b9_2.doc, dec(2) replace keep(norms2 1.treat 1.treat#c.norms2) stats(coef se pval) ctitle(Full sample)
{txt}{stata `"shellout using `"b9_2.doc"'"':b9_2.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b9_2.txt""':seeout}

{com}.         margins, dydx(treat) at(norms2=(0(.25)1))
{res}
{txt}{col 1}Conditional marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,590}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.treat}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 6:norms2} = {res:{ralign 3:0}}
{lalign 7:2._at: }{space 0}{lalign 6:norms2} = {res:{ralign 3:.25}}
{lalign 7:3._at: }{space 0}{lalign 6:norms2} = {res:{ralign 3:.5}}
{lalign 7:4._at: }{space 0}{lalign 6:norms2} = {res:{ralign 3:.75}}
{lalign 7:5._at: }{space 0}{lalign 6:norms2} = {res:{ralign 3:1}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.treat     {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.treat      {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.2476119{col 26}{space 2} .1456015{col 37}{space 1}   -1.70{col 46}{space 3}0.089{col 54}{space 4}-.5332035{col 67}{space 3} .0379797
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.1625902{col 26}{space 2} .0992588{col 37}{space 1}   -1.64{col 46}{space 3}0.102{col 54}{space 4}-.3572824{col 67}{space 3}  .032102
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.0775684{col 26}{space 2} .1165478{col 37}{space 1}   -0.67{col 46}{space 3}0.506{col 54}{space 4}-.3061724{col 67}{space 3} .1510355
{txt}{space 10}4  {c |}{col 14}{res}{space 2} .0074533{col 26}{space 2} .1799807{col 37}{space 1}    0.04{col 46}{space 3}0.967{col 54}{space 4}-.3455717{col 67}{space 3} .3604783
{txt}{space 10}5  {c |}{col 14}{res}{space 2}  .092475{col 26}{space 2} .2574515{col 37}{space 1}    0.36{col 46}{space 3}0.719{col 54}{space 4}-.4125059{col 67}{space 3}  .597456
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) xtitle("Approval of Vote Buying (citizen-centered)") ytitle(Predicted Trust in Elections) title(Full Sample)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:norms2}{p_end}
{res}{txt}
{com}.         graph save Graph "b9_2_full.gph", replace
{res}{txt}file {bf:b9_2_full.gph} saved

{com}. reg b47a c.norms2##i.treat if speed==0

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,544
{txt}{hline 13}{c +}{hline 34}   F(3, 1540)      = {res}     7.69
{txt}       Model {c |} {res} 85.3745092         3  28.4581697   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 5699.42795     1,540  3.70092724   {txt}R-squared       ={res}    0.0148
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0128
{txt}       Total {c |} {res} 5784.80246     1,543  3.74906187   {txt}Root MSE        =   {res} 1.9238

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          b47a{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}norms2 {c |}{col 16}{res}{space 2} .6175044{col 28}{space 2} .2519343{col 39}{space 1}    2.45{col 48}{space 3}0.014{col 56}{space 4} .1233339{col 69}{space 3} 1.111675
{txt}{space 7}1.treat {c |}{col 16}{res}{space 2} -.250128{col 28}{space 2} .1468163{col 39}{space 1}   -1.70{col 48}{space 3}0.089{col 56}{space 4} -.538109{col 69}{space 3}  .037853
{txt}{space 14} {c |}
treat#c.norms2 {c |}
{space 12}1  {c |}{col 16}{res}{space 2} .3536696{col 28}{space 2} .3557685{col 39}{space 1}    0.99{col 48}{space 3}0.320{col 56}{space 4}-.3441723{col 69}{space 3} 1.051511
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2} 3.102438{col 28}{space 2} .1038046{col 39}{space 1}   29.89{col 48}{space 3}0.000{col 56}{space 4} 2.898824{col 69}{space 3} 3.306051
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using b9_2.doc, dec(2) append keep(norms2 1.treat 1.treat#c.norms2) stats(coef se pval) ctitle(No speeders)
{txt}{stata `"shellout using `"b9_2.doc"'"':b9_2.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b9_2.txt""':seeout}

{com}. reg b47a c.norms2##i.treat if exp1c!=1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,255
{txt}{hline 13}{c +}{hline 34}   F(3, 1251)      = {res}     6.46
{txt}       Model {c |} {res} 70.1440646         3  23.3813549   {txt}Prob > F        ={res}    0.0002
{txt}    Residual {c |} {res} 4524.90454     1,251  3.61703001   {txt}R-squared       ={res}    0.0153
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0129
{txt}       Total {c |} {res} 4595.04861     1,254  3.66431308   {txt}Root MSE        =   {res} 1.9018

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          b47a{col 16}{c |} Coefficient{col 28}  Std. err.{col 40}      t{col 48}   P>|t|{col 56}     [95% con{col 69}f. interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}norms2 {c |}{col 16}{res}{space 2} .6204418{col 28}{space 2} .2400993{col 39}{space 1}    2.58{col 48}{space 3}0.010{col 56}{space 4} .1494001{col 69}{space 3} 1.091484
{txt}{space 7}1.treat {c |}{col 16}{res}{space 2}-.3486014{col 28}{space 2} .1711912{col 39}{space 1}   -2.04{col 48}{space 3}0.042{col 56}{space 4}-.6844549{col 69}{space 3}-.0127478
{txt}{space 14} {c |}
treat#c.norms2 {c |}
{space 12}1  {c |}{col 16}{res}{space 2} .2831925{col 28}{space 2}  .411177{col 39}{space 1}    0.69{col 48}{space 3}0.491{col 56}{space 4}  -.52348{col 69}{space 3} 1.089865
{txt}{space 14} {c |}
{space 9}_cons {c |}{col 16}{res}{space 2}  3.11379{col 28}{space 2} .1010295{col 39}{space 1}   30.82{col 48}{space 3}0.000{col 56}{space 4} 2.915584{col 69}{space 3} 3.311996
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using b9_2.doc, dec(2) append keep(norms2 1.treat 1.treat#c.norms2) stats(coef se pval) ctitle(Unsaturated)
{txt}{stata `"shellout using `"b9_2.doc"'"':b9_2.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b9_2.txt""':seeout}

{com}.         margins, dydx(treat) at(norms2=(0(.25)1))
{res}
{txt}{col 1}Conditional marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:1,255}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.treat}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 6:norms2} = {res:{ralign 3:0}}
{lalign 7:2._at: }{space 0}{lalign 6:norms2} = {res:{ralign 3:.25}}
{lalign 7:3._at: }{space 0}{lalign 6:norms2} = {res:{ralign 3:.5}}
{lalign 7:4._at: }{space 0}{lalign 6:norms2} = {res:{ralign 3:.75}}
{lalign 7:5._at: }{space 0}{lalign 6:norms2} = {res:{ralign 3:1}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.treat     {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.treat      {txt}{c |}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-.3486014{col 26}{space 2} .1711912{col 37}{space 1}   -2.04{col 46}{space 3}0.042{col 54}{space 4}-.6844549{col 67}{space 3}-.0127478
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-.2778033{col 26}{space 2} .1152169{col 37}{space 1}   -2.41{col 46}{space 3}0.016{col 54}{space 4}-.5038429{col 67}{space 3}-.0517637
{txt}{space 10}3  {c |}{col 14}{res}{space 2}-.2070051{col 26}{space 2} .1355608{col 37}{space 1}   -1.53{col 46}{space 3}0.127{col 54}{space 4}-.4729567{col 67}{space 3} .0589464
{txt}{space 10}4  {c |}{col 14}{res}{space 2} -.136207{col 26}{space 2} .2112151{col 37}{space 1}   -0.64{col 46}{space 3}0.519{col 54}{space 4} -.550582{col 67}{space 3}  .278168
{txt}{space 10}5  {c |}{col 14}{res}{space 2}-.0654089{col 26}{space 2} .3032825{col 37}{space 1}   -0.22{col 46}{space 3}0.829{col 54}{space 4}-.6604073{col 67}{space 3} .5295895
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 78}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, yline(0) xtitle("Approval of Vote Buying (citizen-centered)") ytitle(Predicted Trust in Elections) title(Unsaturated Sample)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:norms2}{p_end}
{res}{txt}
{com}.         graph save Graph "b9_2_unsat.gph", replace      
{res}{txt}file {bf:b9_2_unsat.gph} saved

{com}. graph combine b9_2_full.gph b9_2_unsat.gph
{res}{txt}
{com}.         graph save Graph "b9_2.gph", replace
{res}{txt}file {bf:b9_2.gph} saved

{com}. 
. 
. * B12. placebo test with trust in congress, church and health ministry
.         * trust in congress
. reg b13 treat

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,589
{txt}{hline 13}{c +}{hline 34}   F(1, 1587)      = {res}     0.00
{txt}       Model {c |} {res} .000840425         1  .000840425   {txt}Prob > F        ={res}    0.9855
{txt}    Residual {c |} {res} 4033.08915     1,587  2.54132902   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0006
{txt}       Total {c |} {res} 4033.08999     1,588  2.53972922   {txt}Root MSE        =   {res} 1.5942

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         b13{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}treat {c |}{col 14}{res}{space 2}-.0014547{col 26}{space 2} .0799947{col 37}{space 1}   -0.02{col 46}{space 3}0.985{col 54}{space 4}-.1583611{col 67}{space 3} .1554517
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.957921{col 26}{space 2} .0560822{col 37}{space 1}   52.74{col 46}{space 3}0.000{col 54}{space 4} 2.847918{col 67}{space 3} 3.067924
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using b12.doc, dec(2) replace keep(treat) stats(coef se pval) ctitle(Congress, Full sample)
{txt}{stata `"shellout using `"b12.doc"'"':b12.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b12.txt""':seeout}

{com}. reg b13 treat if speed==0

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,543
{txt}{hline 13}{c +}{hline 34}   F(1, 1541)      = {res}     0.00
{txt}       Model {c |} {res} .009683192         1  .009683192   {txt}Prob > F        ={res}    0.9506
{txt}    Residual {c |} {res} 3887.85681     1,541  2.52294407   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0006
{txt}       Total {c |} {res} 3887.86649     1,542   2.5213142   {txt}Root MSE        =   {res} 1.5884

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         b13{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}treat {c |}{col 14}{res}{space 2}-.0050105{col 26}{space 2} .0808763{col 37}{space 1}   -0.06{col 46}{space 3}0.951{col 54}{space 4}-.1636497{col 67}{space 3} .1536288
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.944801{col 26}{space 2} .0569095{col 37}{space 1}   51.75{col 46}{space 3}0.000{col 54}{space 4} 2.833173{col 67}{space 3} 3.056429
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using b12.doc, dec(2) append keep(treat) stats(coef se pval) ctitle(Congress, No speeders)
{txt}{stata `"shellout using `"b12.doc"'"':b12.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b12.txt""':seeout}

{com}. reg b13 treat if exp1c!=1 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,255
{txt}{hline 13}{c +}{hline 34}   F(1, 1253)      = {res}     0.03
{txt}       Model {c |} {res} .074471423         1  .074471423   {txt}Prob > F        ={res}    0.8651
{txt}    Residual {c |} {res}   3233.057     1,253  2.58025299   {txt}R-squared       ={res}    0.0000
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0008
{txt}       Total {c |} {res} 3233.13147     1,254  2.57825476   {txt}Root MSE        =   {res} 1.6063

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         b13{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}treat {c |}{col 14}{res}{space 2}-.0160863{col 26}{space 2} .0946877{col 37}{space 1}   -0.17{col 46}{space 3}0.865{col 54}{space 4}-.2018503{col 67}{space 3} .1696776
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.957921{col 26}{space 2}   .05651{col 37}{space 1}   52.34{col 46}{space 3}0.000{col 54}{space 4} 2.847056{col 67}{space 3} 3.068785
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using b12.doc, dec(2) append keep(treat) stats(coef se pval) ctitle(Congress, Unsaturated)
{txt}{stata `"shellout using `"b12.doc"'"':b12.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b12.txt""':seeout}

{com}.         * trust in catholic church
. reg b20 treat

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,590
{txt}{hline 13}{c +}{hline 34}   F(1, 1588)      = {res}     0.42
{txt}       Model {c |} {res} 1.80296792         1  1.80296792   {txt}Prob > F        ={res}    0.5186
{txt}    Residual {c |} {res} 6868.08697     1,588  4.32499179   {txt}R-squared       ={res}    0.0003
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0004
{txt}       Total {c |} {res} 6869.88994     1,589  4.32340462   {txt}Root MSE        =   {res} 2.0797

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         b20{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}treat {c |}{col 14}{res}{space 2} .0673571{col 26}{space 2} .1043235{col 37}{space 1}    0.65{col 46}{space 3}0.519{col 54}{space 4}-.1372691{col 67}{space 3} .2719834
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.064356{col 26}{space 2} .0731622{col 37}{space 1}   41.88{col 46}{space 3}0.000{col 54}{space 4} 2.920852{col 67}{space 3} 3.207861
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using b12.doc, dec(2) append keep(treat) stats(coef se pval) ctitle(Church, Full sample)
{txt}{stata `"shellout using `"b12.doc"'"':b12.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b12.txt""':seeout}

{com}. reg b20 treat if speed==0

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,544
{txt}{hline 13}{c +}{hline 34}   F(1, 1542)      = {res}     0.20
{txt}       Model {c |} {res} .852485487         1  .852485487   {txt}Prob > F        ={res}    0.6576
{txt}    Residual {c |} {res} 6687.27122     1,542  4.33675176   {txt}R-squared       ={res}    0.0001
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0005
{txt}       Total {c |} {res}  6688.1237     1,543  4.33449365   {txt}Root MSE        =   {res} 2.0825

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         b20{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}treat {c |}{col 14}{res}{space 2} .0469967{col 26}{space 2} .1060002{col 37}{space 1}    0.44{col 46}{space 3}0.658{col 54}{space 4}-.1609229{col 67}{space 3} .2549164
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.068036{col 26}{space 2} .0746128{col 37}{space 1}   41.12{col 46}{space 3}0.000{col 54}{space 4} 2.921683{col 67}{space 3} 3.214389
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using b12.doc, dec(2) append keep(treat) stats(coef se pval) ctitle(Church, No speeders)
{txt}{stata `"shellout using `"b12.doc"'"':b12.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b12.txt""':seeout}

{com}. reg b20 treat if exp1c!=1 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,255
{txt}{hline 13}{c +}{hline 34}   F(1, 1253)      = {res}     0.87
{txt}       Model {c |} {res} 3.78054269         1  3.78054269   {txt}Prob > F        ={res}    0.3505
{txt}    Residual {c |} {res} 5430.33579     1,253  4.33386735   {txt}R-squared       ={res}    0.0007
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}   -0.0001
{txt}       Total {c |} {res} 5434.11633     1,254   4.3334261   {txt}Root MSE        =   {res} 2.0818

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         b20{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}treat {c |}{col 14}{res}{space 2} .1146145{col 26}{space 2} .1227157{col 37}{space 1}    0.93{col 46}{space 3}0.350{col 54}{space 4}-.1261365{col 67}{space 3} .3553654
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.064356{col 26}{space 2} .0732373{col 37}{space 1}   41.84{col 46}{space 3}0.000{col 54}{space 4} 2.920675{col 67}{space 3} 3.208038
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using b12.doc, dec(2) append keep(treat) stats(coef se pval) ctitle(Church, Unsaturated)
{txt}{stata `"shellout using `"b12.doc"'"':b12.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b12.txt""':seeout}

{com}.         * trust in health ministry
. reg b81 treat

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,591
{txt}{hline 13}{c +}{hline 34}   F(1, 1589)      = {res}     2.78
{txt}       Model {c |} {res} 8.23817601         1  8.23817601   {txt}Prob > F        ={res}    0.0959
{txt}    Residual {c |} {res} 4717.19614     1,589  2.96865711   {txt}R-squared       ={res}    0.0017
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0011
{txt}       Total {c |} {res} 4725.43432     1,590  2.97197127   {txt}Root MSE        =   {res}  1.723

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         b81{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}treat {c |}{col 14}{res}{space 2} .1439342{col 26}{space 2} .0864029{col 37}{space 1}    1.67{col 46}{space 3}0.096{col 54}{space 4}-.0255415{col 67}{space 3}   .31341
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  3.94802{col 26}{space 2} .0606142{col 37}{space 1}   65.13{col 46}{space 3}0.000{col 54}{space 4} 3.829128{col 67}{space 3} 4.066912
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using b12.doc, dec(2) append keep(treat) stats(coef se pval) ctitle(Health, Full sample)
{txt}{stata `"shellout using `"b12.doc"'"':b12.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b12.txt""':seeout}

{com}. reg b81 treat if speed==0

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,545
{txt}{hline 13}{c +}{hline 34}   F(1, 1543)      = {res}     3.28
{txt}       Model {c |} {res} 9.66892261         1  9.66892261   {txt}Prob > F        ={res}    0.0704
{txt}    Residual {c |} {res} 4549.89354     1,543  2.94873204   {txt}R-squared       ={res}    0.0021
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0015
{txt}       Total {c |} {res} 4559.56246     1,544  2.95308449   {txt}Root MSE        =   {res} 1.7172

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         b81{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}treat {c |}{col 14}{res}{space 2} .1582232{col 26}{space 2} .0873773{col 37}{space 1}    1.81{col 46}{space 3}0.070{col 54}{space 4}-.0131677{col 67}{space 3} .3296141
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.938383{col 26}{space 2} .0615246{col 37}{space 1}   64.01{col 46}{space 3}0.000{col 54}{space 4} 3.817702{col 67}{space 3} 4.059063
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using b12.doc, dec(2) append keep(treat) stats(coef se pval) ctitle(Health, No speeders)
{txt}{stata `"shellout using `"b12.doc"'"':b12.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b12.txt""':seeout}

{com}. reg b81 treat if exp1c!=1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     1,257
{txt}{hline 13}{c +}{hline 34}   F(1, 1255)      = {res}     2.34
{txt}       Model {c |} {res} 7.08310152         1  7.08310152   {txt}Prob > F        ={res}    0.1266
{txt}    Residual {c |} {res} 3803.89701     1,255  3.03099363   {txt}R-squared       ={res}    0.0019
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0011
{txt}       Total {c |} {res} 3810.98011     1,256  3.03421983   {txt}Root MSE        =   {res}  1.741

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         b81{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}treat {c |}{col 14}{res}{space 2} .1566573{col 26}{space 2} .1024782{col 37}{space 1}    1.53{col 46}{space 3}0.127{col 54}{space 4}-.0443901{col 67}{space 3} .3577046
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  3.94802{col 26}{space 2} .0612473{col 37}{space 1}   64.46{col 46}{space 3}0.000{col 54}{space 4} 3.827861{col 67}{space 3} 4.068178
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         outreg2 using b12.doc, dec(2) append keep(treat) stats(coef se pval) ctitle(Health, Unsaturated)
{txt}{stata `"shellout using `"b12.doc"'"':b12.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b12.txt""':seeout}

{com}. 
.         
.         
. *** B10-11 diff-in-diff analyses
. gen dv0=b47b
{txt}(38 missing values generated)

{com}.         gen dv1=b47a
{txt}(119 missing values generated)

{com}.         reshape long dv, i(responseid) j(post_treatment)
{txt}(j = 0 1)

Data{col 36}Wide{col 43}->{col 48}Long
{hline 77}
Number of observations     {res}       1,709   {txt}->   {res}3,418       
{txt}Number of variables        {res}          36   {txt}->   {res}36          
{txt}j variable (2 values)                     ->   {res}post_treatment
{txt}xij variables:
                                {res}dv0 dv1   {txt}->   {res}dv
{txt}{hline 77}

{com}. lab def post_treatment 0"Pre" 1"Post"
{txt}
{com}.         lab val post_treatment post_treatment
{txt}
{com}. 
. * B10 (H1 diff-in-diff) 
. reg dv treat##post_treatment

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     3,261
{txt}{hline 13}{c +}{hline 34}   F(3, 3257)      = {res}    18.16
{txt}       Model {c |} {res} 208.656208         3  69.5520695   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 12476.2496     3,257  3.83059553   {txt}R-squared       ={res}    0.0164
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0155
{txt}       Total {c |} {res} 12684.9059     3,260  3.89107542   {txt}Root MSE        =   {res} 1.9572

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                  dv{col 22}{c |} Coefficient{col 34}  Std. err.{col 46}      t{col 54}   P>|t|{col 62}     [95% con{col 75}f. interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}1.treat {c |}{col 22}{res}{space 2}  -.10177{col 34}{space 2} .0959131{col 45}{space 1}   -1.06{col 54}{space 3}0.289{col 62}{space 4}-.2898261{col 75}{space 3} .0862861
{txt}{space 20} {c |}
{space 6}post_treatment {c |}
{space 15}Post  {c |}{col 22}{res}{space 2} -.467491{col 34}{space 2} .0952838{col 45}{space 1}   -4.91{col 54}{space 3}0.000{col 62}{space 4}-.6543132{col 75}{space 3}-.2806688
{txt}{space 20} {c |}
treat#post_treatment {c |}
{space 13}1#Post  {c |}{col 22}{res}{space 2}-.0426743{col 34}{space 2} .1372538{col 45}{space 1}   -0.31{col 54}{space 3}0.756{col 62}{space 4}-.3117869{col 75}{space 3} .2264383
{txt}{space 20} {c |}
{space 15}_cons {c |}{col 22}{res}{space 2} 3.776897{col 34}{space 2} .0658647{col 45}{space 1}   57.34{col 54}{space 3}0.000{col 62}{space 4} 3.647756{col 75}{space 3} 3.906037
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         lincom 1.treat + 1.treat#1.post_treatment

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.treat + 1.treat#1.post_treatment = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          dv{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.1444443{col 26}{space 2} .0981799{col 37}{space 1}   -1.47{col 46}{space 3}0.141{col 54}{space 4}-.3369449{col 67}{space 3} .0480563
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using b10.doc, dec(2) replace ctitle(Full Sample) ///
>                 keep(1.treat 1.post_treatment 1.treat#1.post_treatment)
{txt}{stata `"shellout using `"b10.doc"'"':b10.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b10.txt""':seeout}

{com}. reg dv treat##post_treatment if speed==0

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     3,113
{txt}{hline 13}{c +}{hline 34}   F(3, 3109)      = {res}    17.36
{txt}       Model {c |} {res} 198.325101         3   66.108367   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 11839.1166     3,109  3.80801434   {txt}R-squared       ={res}    0.0165
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0155
{txt}       Total {c |} {res} 12037.4417     3,112  3.86807252   {txt}Root MSE        =   {res} 1.9514

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                  dv{col 22}{c |} Coefficient{col 34}  Std. err.{col 46}      t{col 54}   P>|t|{col 62}     [95% con{col 75}f. interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}1.treat {c |}{col 22}{res}{space 2}-.0778156{col 34}{space 2} .0985544{col 45}{space 1}   -0.79{col 54}{space 3}0.430{col 62}{space 4}-.2710538{col 75}{space 3} .1154227
{txt}{space 20} {c |}
{space 6}post_treatment {c |}
{space 15}Post  {c |}{col 22}{res}{space 2}-.4591874{col 34}{space 2} .0981656{col 45}{space 1}   -4.68{col 54}{space 3}0.000{col 62}{space 4}-.6516634{col 75}{space 3}-.2667114
{txt}{space 20} {c |}
treat#post_treatment {c |}
{space 13}1#Post  {c |}{col 22}{res}{space 2}-.0645406{col 34}{space 2} .1399253{col 45}{space 1}   -0.46{col 54}{space 3}0.645{col 62}{space 4} -.338896{col 75}{space 3} .2098148
{txt}{space 20} {c |}
{space 15}_cons {c |}{col 22}{res}{space 2}  3.75187{col 34}{space 2} .0689068{col 45}{space 1}   54.45{col 54}{space 3}0.000{col 62}{space 4} 3.616763{col 75}{space 3} 3.886978
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         lincom 1.treat + 1.treat#1.post_treatment

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.treat + 1.treat#1.post_treatment = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          dv{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.1423561{col 26}{space 2} .0993284{col 37}{space 1}   -1.43{col 46}{space 3}0.152{col 54}{space 4}-.3371121{col 67}{space 3} .0523998
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using b10.doc, dec(2) append ctitle(No Speeders) ///
>                 keep(1.treat 1.post_treatment 1.treat#1.post_treatment)
{txt}{stata `"shellout using `"b10.doc"'"':b10.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b10.txt""':seeout}

{com}. reg dv treat##post_treatment if exp1c!=1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     2,591
{txt}{hline 13}{c +}{hline 34}   F(3, 2587)      = {res}    18.51
{txt}       Model {c |} {res} 209.689376         3  69.8964587   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 9770.00958     2,587  3.77657889   {txt}R-squared       ={res}    0.0210
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0199
{txt}       Total {c |} {res} 9979.69896     2,590  3.85316562   {txt}Root MSE        =   {res} 1.9433

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                  dv{col 22}{c |} Coefficient{col 34}  Std. err.{col 46}      t{col 54}   P>|t|{col 62}     [95% con{col 75}f. interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 13}1.treat {c |}{col 22}{res}{space 2}-.0572501{col 34}{space 2} .1123112{col 45}{space 1}   -0.51{col 54}{space 3}0.610{col 62}{space 4}-.2774791{col 75}{space 3} .1629788
{txt}{space 20} {c |}
{space 6}post_treatment {c |}
{space 15}Post  {c |}{col 22}{res}{space 2} -.467491{col 34}{space 2} .0946096{col 45}{space 1}   -4.94{col 54}{space 3}0.000{col 62}{space 4}-.6530091{col 75}{space 3}-.2819729
{txt}{space 20} {c |}
treat#post_treatment {c |}
{space 13}1#Post  {c |}{col 22}{res}{space 2}-.2029388{col 34}{space 2}  .160426{col 45}{space 1}   -1.26{col 54}{space 3}0.206{col 62}{space 4}-.5175152{col 75}{space 3} .1116376
{txt}{space 20} {c |}
{space 15}_cons {c |}{col 22}{res}{space 2} 3.776897{col 34}{space 2} .0653987{col 45}{space 1}   57.75{col 54}{space 3}0.000{col 62}{space 4} 3.648658{col 75}{space 3} 3.905136
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         lincom 1.treat + 1.treat#1.post_treatment

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.treat + 1.treat#1.post_treatment = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          dv{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.2601889{col 26}{space 2} .1145544{col 37}{space 1}   -2.27{col 46}{space 3}0.023{col 54}{space 4}-.4848165{col 67}{space 3}-.0355614
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using b10.doc, dec(2)  append ctitle(Unsaturated) ///
>                 keep(1.treat 1.post_treatment 1.treat#1.post_treatment)
{txt}{stata `"shellout using `"b10.doc"'"':b10.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b10.txt""':seeout}

{com}. margins treat, at(post_treatment=(0 1))
{res}
{txt}{col 1}Adjusted predictions{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:2,591}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}
{lalign 7:1._at: }{space 0}{lalign 14:post_treatment} = {res:{ralign 1:0}}
{lalign 7:2._at: }{space 0}{lalign 14:post_treatment} = {res:{ralign 1:1}}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}_at#treat {c |}
{space 8}1 0  {c |}{col 14}{res}{space 2} 3.776897{col 26}{space 2} .0653987{col 37}{space 1}   57.75{col 46}{space 3}0.000{col 54}{space 4} 3.648658{col 67}{space 3} 3.905136
{txt}{space 8}1 1  {c |}{col 14}{res}{space 2} 3.719647{col 26}{space 2} .0913062{col 37}{space 1}   40.74{col 46}{space 3}0.000{col 54}{space 4} 3.540606{col 67}{space 3} 3.898687
{txt}{space 8}2 0  {c |}{col 14}{res}{space 2} 3.309406{col 26}{space 2} .0683665{col 37}{space 1}   48.41{col 46}{space 3}0.000{col 54}{space 4} 3.175347{col 67}{space 3} 3.443465
{txt}{space 8}2 1  {c |}{col 14}{res}{space 2} 3.049217{col 26}{space 2} .0919169{col 37}{space 1}   33.17{col 46}{space 3}0.000{col 54}{space 4} 2.868979{col 67}{space 3} 3.229455
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         marginsplot, xtitle(DV measure) ytitle(Predicted Trust in Elections) title("")
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:post_treatment treat}{p_end}
{res}{txt}
{com}.         graph save Graph "b10.gph", replace
{res}{txt}file {bf:b10.gph} saved

{com}. 
.         
. *  B11 (H2 diff-in-diff) 
. reg dv c.norm##i.treat##post_treatment

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     3,250
{txt}{hline 13}{c +}{hline 34}   F(7, 3242)      = {res}    26.83
{txt}       Model {c |} {res} 692.500933         7  98.9287047   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 11953.4563     3,242   3.6870624   {txt}R-squared       ={res}    0.0548
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0527
{txt}       Total {c |} {res} 12645.9572     3,249  3.89226138   {txt}Root MSE        =   {res} 1.9202

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                         dv{col 29}{c |} Coefficient{col 41}  Std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}norm {c |}{col 29}{res}{space 2}  1.77519{col 41}{space 2} .2445548{col 52}{space 1}    7.26{col 61}{space 3}0.000{col 69}{space 4} 1.295692{col 82}{space 3} 2.254688
{txt}{space 20}1.treat {c |}{col 29}{res}{space 2}-.1483533{col 41}{space 2}  .140625{col 52}{space 1}   -1.05{col 61}{space 3}0.292{col 69}{space 4}-.4240761{col 82}{space 3} .1273695
{txt}{space 27} {c |}
{space 15}treat#c.norm {c |}
{space 25}1  {c |}{col 29}{res}{space 2} .1664967{col 41}{space 2} .3561169{col 52}{space 1}    0.47{col 61}{space 3}0.640{col 69}{space 4}-.5317402{col 82}{space 3} .8647336
{txt}{space 27} {c |}
{space 13}post_treatment {c |}
{space 22}Post  {c |}{col 29}{res}{space 2}-.1277188{col 41}{space 2} .1389439{col 52}{space 1}   -0.92{col 61}{space 3}0.358{col 69}{space 4}-.4001455{col 82}{space 3} .1447078
{txt}{space 27} {c |}
{space 6}post_treatment#c.norm {c |}
{space 22}Post  {c |}{col 29}{res}{space 2}-1.133057{col 41}{space 2} .3547932{col 52}{space 1}   -3.19{col 61}{space 3}0.001{col 69}{space 4}-1.828699{col 82}{space 3}-.4374159
{txt}{space 27} {c |}
{space 7}treat#post_treatment {c |}
{space 20}1#Post  {c |}{col 29}{res}{space 2}-.1275326{col 41}{space 2} .2007369{col 52}{space 1}   -0.64{col 61}{space 3}0.525{col 69}{space 4}-.5211165{col 82}{space 3} .2660514
{txt}{space 27} {c |}
treat#post_treatment#c.norm {c |}
{space 20}1#Post  {c |}{col 29}{res}{space 2} .2717658{col 41}{space 2} .5109209{col 52}{space 1}    0.53{col 61}{space 3}0.595{col 69}{space 4}-.7299947{col 82}{space 3} 1.273526
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2} 3.253048{col 41}{space 2} .0965023{col 52}{space 1}   33.71{col 61}{space 3}0.000{col 69}{space 4} 3.063836{col 82}{space 3}  3.44226
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         lincom 1.treat#1.post_treatment + norm#1.treat#1.post_treatment

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.treat#1.post_treatment + 1.treat#1.post_treatment#c.norm = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          dv{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .1442333{col 26}{space 2} .3865231{col 37}{space 1}    0.37{col 46}{space 3}0.709{col 54}{space 4}-.6136211{col 67}{space 3} .9020876
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using b11.doc, dec(2) replace ctitle(Full Sample) ///
>                 keep(norm 1.treat 1.treat#c.norm 1.post_treatment 1.post_treatment#c.norm ///
>                         1.treat#1.post_treatment 1.treat#1.post_treatment#c.norm)       
{txt}{stata `"shellout using `"b11.doc"'"':b11.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b11.txt""':seeout}

{com}. reg dv c.norm##i.treat##post_treatment if speed==0

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     3,111
{txt}{hline 13}{c +}{hline 34}   F(7, 3103)      = {res}    24.62
{txt}       Model {c |} {res}  633.03841         7  90.4340586   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res}   11397.78     3,103  3.67314856   {txt}R-squared       ={res}    0.0526
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0505
{txt}       Total {c |} {res} 12030.8184     3,110  3.86843035   {txt}Root MSE        =   {res} 1.9165

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                         dv{col 29}{c |} Coefficient{col 41}  Std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}norm {c |}{col 29}{res}{space 2} 1.667942{col 41}{space 2} .2629712{col 52}{space 1}    6.34{col 61}{space 3}0.000{col 69}{space 4} 1.152327{col 82}{space 3} 2.183557
{txt}{space 20}1.treat {c |}{col 29}{res}{space 2}-.1821577{col 41}{space 2} .1436072{col 52}{space 1}   -1.27{col 61}{space 3}0.205{col 69}{space 4}-.4637325{col 82}{space 3} .0994171
{txt}{space 27} {c |}
{space 15}treat#c.norm {c |}
{space 25}1  {c |}{col 29}{res}{space 2} .3228725{col 41}{space 2} .3734958{col 52}{space 1}    0.86{col 61}{space 3}0.387{col 69}{space 4}-.4094514{col 82}{space 3} 1.055196
{txt}{space 27} {c |}
{space 13}post_treatment {c |}
{space 22}Post  {c |}{col 29}{res}{space 2}-.1690964{col 41}{space 2}  .142404{col 52}{space 1}   -1.19{col 61}{space 3}0.235{col 69}{space 4}-.4483121{col 82}{space 3} .1101193
{txt}{space 27} {c |}
{space 6}post_treatment#c.norm {c |}
{space 22}Post  {c |}{col 29}{res}{space 2}-1.027737{col 41}{space 2} .3749644{col 52}{space 1}   -2.74{col 61}{space 3}0.006{col 69}{space 4} -1.76294{col 82}{space 3}-.2925332
{txt}{space 27} {c |}
{space 7}treat#post_treatment {c |}
{space 20}1#Post  {c |}{col 29}{res}{space 2}-.0906164{col 41}{space 2} .2038325{col 52}{space 1}   -0.44{col 61}{space 3}0.657{col 69}{space 4}-.4902766{col 82}{space 3} .3090437
{txt}{space 27} {c |}
treat#post_treatment#c.norm {c |}
{space 20}1#Post  {c |}{col 29}{res}{space 2} .1094341{col 41}{space 2} .5305216{col 52}{space 1}    0.21{col 61}{space 3}0.837{col 69}{space 4} -.930775{col 82}{space 3} 1.149643
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2} 3.283237{col 41}{space 2} .1000352{col 52}{space 1}   32.82{col 61}{space 3}0.000{col 69}{space 4} 3.087095{col 82}{space 3} 3.479379
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         lincom 1.treat#1.post_treatment + norm#1.treat#1.post_treatment

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.treat#1.post_treatment + 1.treat#1.post_treatment#c.norm = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          dv{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .0188176{col 26}{space 2} .4041426{col 37}{space 1}    0.05{col 46}{space 3}0.963{col 54}{space 4}-.7735964{col 67}{space 3} .8112316
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using b11.doc, dec(2) append ctitle(No Speeders) ///
>                 keep(norm 1.treat 1.treat#c.norm 1.post_treatment 1.post_treatment#c.norm ///
>                         1.treat#1.post_treatment 1.treat#1.post_treatment#c.norm) 
{txt}{stata `"shellout using `"b11.doc"'"':b11.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b11.txt""':seeout}

{com}. reg dv c.norm##i.treat##post_treatment if exp1c!=1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     2,580
{txt}{hline 13}{c +}{hline 34}   F(7, 2572)      = {res}    23.03
{txt}       Model {c |} {res} 586.361009         7  83.7658584   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 9354.48395     2,572  3.63704664   {txt}R-squared       ={res}    0.0590
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0564
{txt}       Total {c |} {res} 9940.84496     2,579  3.85453469   {txt}Root MSE        =   {res} 1.9071

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                         dv{col 29}{c |} Coefficient{col 41}  Std. err.{col 53}      t{col 61}   P>|t|{col 69}     [95% con{col 82}f. interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}norm {c |}{col 29}{res}{space 2}  1.77519{col 41}{space 2} .2428904{col 52}{space 1}    7.31{col 61}{space 3}0.000{col 69}{space 4} 1.298909{col 82}{space 3} 2.251471
{txt}{space 20}1.treat {c |}{col 29}{res}{space 2}-.1511471{col 41}{space 2} .1673599{col 52}{space 1}   -0.90{col 61}{space 3}0.367{col 69}{space 4}-.4793209{col 82}{space 3} .1770267
{txt}{space 27} {c |}
{space 15}treat#c.norm {c |}
{space 25}1  {c |}{col 29}{res}{space 2} .2919484{col 41}{space 2} .4241025{col 52}{space 1}    0.69{col 61}{space 3}0.491{col 69}{space 4}-.5396686{col 82}{space 3} 1.123565
{txt}{space 27} {c |}
{space 13}post_treatment {c |}
{space 22}Post  {c |}{col 29}{res}{space 2}-.1277188{col 41}{space 2} .1379982{col 52}{space 1}   -0.93{col 61}{space 3}0.355{col 69}{space 4}-.3983178{col 82}{space 3} .1428801
{txt}{space 27} {c |}
{space 6}post_treatment#c.norm {c |}
{space 22}Post  {c |}{col 29}{res}{space 2}-1.133057{col 41}{space 2} .3523785{col 52}{space 1}   -3.22{col 61}{space 3}0.001{col 69}{space 4}-1.824032{col 82}{space 3}-.4420831
{txt}{space 27} {c |}
{space 7}treat#post_treatment {c |}
{space 20}1#Post  {c |}{col 29}{res}{space 2}-.2478587{col 41}{space 2} .2385771{col 52}{space 1}   -1.04{col 61}{space 3}0.299{col 69}{space 4}-.7156813{col 82}{space 3} .2199639
{txt}{space 27} {c |}
treat#post_treatment#c.norm {c |}
{space 20}1#Post  {c |}{col 29}{res}{space 2} .1562565{col 41}{space 2} .6078717{col 52}{space 1}    0.26{col 61}{space 3}0.797{col 69}{space 4}-1.035711{col 82}{space 3} 1.348224
{txt}{space 27} {c |}
{space 22}_cons {c |}{col 29}{res}{space 2} 3.253048{col 41}{space 2} .0958456{col 52}{space 1}   33.94{col 61}{space 3}0.000{col 69}{space 4} 3.065106{col 82}{space 3}  3.44099
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}.         lincom 1.treat#1.post_treatment + norm#1.treat#1.post_treatment

{p 0 7}{space 1}{text:( 1)}{space 1} {res}1.treat#1.post_treatment + 1.treat#1.post_treatment#c.norm = 0{p_end}

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          dv{col 14}{c |} Coefficient{col 26}  Std. err.{col 38}      t{col 46}   P>|t|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2}-.0916022{col 26}{space 2} .4568324{col 37}{space 1}   -0.20{col 46}{space 3}0.841{col 54}{space 4}-.9873989{col 67}{space 3} .8041944
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}.         outreg2 using b11.doc, dec(2) append ctitle(Unsaturated) ///
>                 keep(norm 1.treat 1.treat#c.norm 1.post_treatment 1.post_treatment#c.norm ///
>                 1.treat#1.post_treatment 1.treat#1.post_treatment#c.norm)
{txt}{stata `"shellout using `"b11.doc"'"':b11.doc}
{browse `"C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\figures and tables"' :dir}{com} : {txt}{stata `"seeout using "b11.txt""':seeout}

{com}. margins, dydx(post_treatment) at(norm=(0(.25)1) treat=(0 1))
{res}
{txt}{col 1}Conditional marginal effects{col 58}{lalign 13:Number of obs}{col 71} = {res}{ralign 5:2,580}
{txt}{col 1}Model VCE: {res:OLS}

{txt}{p2colset 1 13 13 2}{...}
{p2col:Expression:}{res:Linear prediction, predict()}{p_end}
{p2col:dy/dx wrt:}{res:1.post_treatment}{p_end}
{p2colreset}{...}
{lalign 8:1._at: }{space 0}{lalign 5:norm} = {res:{ralign 3:0}}
{lalign 8:}{space 0}{lalign 5:treat} = {res:{ralign 3:0}}
{lalign 8:2._at: }{space 0}{lalign 5:norm} = {res:{ralign 3:0}}
{lalign 8:}{space 0}{lalign 5:treat} = {res:{ralign 3:1}}
{lalign 8:3._at: }{space 0}{lalign 5:norm} = {res:{ralign 3:.25}}
{lalign 8:}{space 0}{lalign 5:treat} = {res:{ralign 3:0}}
{lalign 8:4._at: }{space 0}{lalign 5:norm} = {res:{ralign 3:.25}}
{lalign 8:}{space 0}{lalign 5:treat} = {res:{ralign 3:1}}
{lalign 8:5._at: }{space 0}{lalign 5:norm} = {res:{ralign 3:.5}}
{lalign 8:}{space 0}{lalign 5:treat} = {res:{ralign 3:0}}
{lalign 8:6._at: }{space 0}{lalign 5:norm} = {res:{ralign 3:.5}}
{lalign 8:}{space 0}{lalign 5:treat} = {res:{ralign 3:1}}
{lalign 8:7._at: }{space 0}{lalign 5:norm} = {res:{ralign 3:.75}}
{lalign 8:}{space 0}{lalign 5:treat} = {res:{ralign 3:0}}
{lalign 8:8._at: }{space 0}{lalign 5:norm} = {res:{ralign 3:.75}}
{lalign 8:}{space 0}{lalign 5:treat} = {res:{ralign 3:1}}
{lalign 8:9._at: }{space 0}{lalign 5:norm} = {res:{ralign 3:1}}
{lalign 8:}{space 0}{lalign 5:treat} = {res:{ralign 3:0}}
{lalign 8:10._at: }{space 0}{lalign 5:norm} = {res:{ralign 3:1}}
{lalign 8:}{space 0}{lalign 5:treat} = {res:{ralign 3:1}}

{res}{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31} Delta-method
{col 19}{c |}      dy/dx{col 31}   std. err.{col 43}      t{col 51}   P>|t|{col 59}     [95% con{col 72}f. interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0.post_treatment {col 19}{txt}{c |}  (base outcome)
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1.post_treatment  {txt}{c |}
{space 14}_at {c |}
{space 15}1  {c |}{col 19}{res}{space 2}-.1277188{col 31}{space 2} .1379982{col 42}{space 1}   -0.93{col 51}{space 3}0.355{col 59}{space 4}-.3983178{col 72}{space 3} .1428801
{txt}{space 15}2  {c |}{col 19}{res}{space 2}-.3755775{col 31}{space 2} .1946163{col 42}{space 1}   -1.93{col 51}{space 3}0.054{col 59}{space 4} -.757198{col 72}{space 3}  .006043
{txt}{space 15}3  {c |}{col 19}{res}{space 2}-.4109832{col 31}{space 2} .0941371{col 42}{space 1}   -4.37{col 51}{space 3}0.000{col 59}{space 4}-.5955753{col 72}{space 3}-.2263911
{txt}{space 15}4  {c |}{col 19}{res}{space 2}-.6197778{col 31}{space 2} .1293008{col 42}{space 1}   -4.79{col 51}{space 3}0.000{col 59}{space 4}-.8733219{col 72}{space 3}-.3662336
{txt}{space 15}5  {c |}{col 19}{res}{space 2}-.6942476{col 31}{space 2} .1191696{col 42}{space 1}   -5.83{col 51}{space 3}0.000{col 59}{space 4}-.9279256{col 72}{space 3}-.4605695
{txt}{space 15}6  {c |}{col 19}{res}{space 2} -.863978{col 31}{space 2} .1619538{col 42}{space 1}   -5.33{col 51}{space 3}0.000{col 59}{space 4}-1.181551{col 72}{space 3}-.5464049
{txt}{space 15}7  {c |}{col 19}{res}{space 2}-.9775119{col 31}{space 2} .1872493{col 42}{space 1}   -5.22{col 51}{space 3}0.000{col 59}{space 4}-1.344687{col 72}{space 3}-.6103372
{txt}{space 15}8  {c |}{col 19}{res}{space 2}-1.108178{col 31}{space 2} .2576947{col 42}{space 1}   -4.30{col 51}{space 3}0.000{col 59}{space 4}-1.613488{col 72}{space 3} -.602868
{txt}{space 15}9  {c |}{col 19}{res}{space 2}-1.260776{col 31}{space 2} .2672912{col 42}{space 1}   -4.72{col 51}{space 3}0.000{col 59}{space 4}-1.784904{col 72}{space 3}-.7366486
{txt}{space 14}10  {c |}{col 19}{res}{space 2}-1.352378{col 31}{space 2} .3704744{col 42}{space 1}   -3.65{col 51}{space 3}0.000{col 59}{space 4}-2.078837{col 72}{space 3}-.6259201
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 0 83}Note: dy/dx for factor levels is the discrete change from the base level.{txt}{p_end}
{res}{txt}
{com}.         marginsplot, xtitle(Approval of Vote Buying)
{res}
{text}{p 0 0 2}Variables that uniquely identify margins: {bf:norm treat}{p_end}
{res}{txt}
{com}.         graph save Graph "b11.gph", replace
{res}{txt}file {bf:b11.gph} saved

{com}.         
.         
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\euiyo\Vanderbilt Dropbox\Noh Emily Euiyoung\VoteBuying\CPS r&r\Final edits\replication materials\replication\logs\log_publicAB.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}14 Feb 2024, 11:26:50
{txt}{.-}
{smcl}
{txt}{sf}{ul off}